Marco Di Ciano
email: m.diciano@innova.puglia.it

Agostino Palmitessa
email: a.palmitessa@innova.puglia.it

Domenico Morgese
email: d.morgese@innova.puglia.it

Denis Havlik
email: denis.havlik@ait.ac.at

Gerald Schimak
email: gerald.schimak@ait.ac.at

Havlik Denis

C2-SENSE -Pilot Scenario for Interoperability Testing in Command & Control Systems for Crises and Disaster Management: Apulia Example

Keywords: Command and Control, Interoperability; Crises-, Disaster-and Emergency Management

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

C2-SENSE system is an environmental application for crises and disaster management. It allows alignment and cooperation between all entities involved in emergency management assuring interoperability between them. To address this challenge, C2-SENSE introduces a novel and practical profiling approach [START_REF] Gençtürk | Profiling ap-proach for the interoperability of Command & Control systems with sensing systems in emergency management[END_REF] that, unlike the conventional profiling approach, which addresses only the first three layers of the interoperability stack [START_REF] Duro | C2-SENSE: The emergency interoperability frame-work and knowledge management[END_REF], involves all the layers of the communication stack in the security field. The work presented in this paper examines in particular the aspects relating to the testing of a pilot application in the region of Apulia (Italy) and the interfacing with information systems of local authorities.

C2-SENSE Overview

Profiling approach for interoperability

An innovative and technological solution to the mentioned problems is represented by profiles that allow you to improve the interoperability between the entities involved in emergency management. The C2-SENSE project develops a profile-based Interoperability Framework [START_REF] Gençtürk | Profiling ap-proach for the interoperability of Command & Control systems with sensing systems in emergency management[END_REF] by integrating existing standards and semantically enriched web services to expose the functionalities of Command & Control (C2) Systems and Sensing Systems involved in the prevention and management of disasters and emergency situations. In a typical C2-SENSE scenario, two main interoperability challenges need to be addressed: the vertical interoperability between Sensing and C2 Systems and the horizontal interoperability among different organizations involved in the prevention and governance of emergency situations. To address the challenge profiling is offered as a practical approach in achieving seamless interoperability by addressing all the layers of the communication stack in the security field. The profile concept aims to eliminate the need for a prior bilateral agreement between any two information exchange partners by defining a standard set of messages/documents, choreographies, business rules and constraints. The profile compliant partners are able to exchange information and services among themselves. This is in contrast to the bilateral agreements that have to be settled between partners for each new exchange partner. Considering the nature of emergency management, in which the responding organizations can change at run time (especially in an international intervention case), these generic profiles provide the needed coordination flexibility in order to deal with the unexpected circumstances and prevent chaotic response in a crisis situation.

Regional involved institutions/organizations

In the following we will give a short overview about the most important organizations, authorities and institutions involved in the Pilot Scenario.

Prefecture

The prefecture of Foggia is the organization that represents the national government in the territory. It has the task of coordinating all the operations related to emergency management and decision-making at the highest level. During emergencies, it interacts with all the involved organizations, in particular with Civil Protection.

Provinces

The test takes place in the area of the Province of Foggia. In general the role of the province is critical in terms of methodological support and governance for local authorities. The improvements made by a modern information system for Civil Protection are important, as they allow local authorities in the Province of Foggia to prepare, adopt and publish plans for Civil Protection that are fully integrated with the risk management process. The C2-SENSE system will be able to ensure access and proper use of the functional system for all organizations involved in the province, providing all the information (static and dynamic) and visual support necessary to represent and describe the land, resources available and the population.

In summary, the information system will strengthen the operational structure of the Civil Protection of the Province of Foggia through an integrated system of information on risk scenarios to be developed and updated in "non-emergency periods". This system will be articulated according to various intervention models for the emergency management, in order to enable the timely response of the province, also in the management and coordination of resources (personnel, materials and means).

Municipalities

The municipalities involved in the tests of the pilot application (as described in section 6) are belonging to the Province of Foggia which has several thousand inhabitants. According to the survey carried out at the beginning of the project, these municipalities replied that they currently do not have an electronic system for handling emergencies, thus we need to simulate these systems adequately.

Regional Civil Protection department

The Civil Protection department has the task to be prepared and handle risk management in accordance with national guidelines. It has a control system (SOIR) "Integrated Regional Control Room" that plays a major role in the coordination and management of emergencies, particularly for all risks affecting the Apulia region.

The SOIR is appointed to handle all the technical decisions, communications and control actions, and, as such, must ensure the continuity, activate procedures and count the occurrence of emergency situations, taking into account the received requests for assistance.

The CFD (Decentralized Functional Centre) monitors the area using specific pluviometric sensors installed in the Region.

Voluntary associations

The high number of criticalities following an event requires a huge deployment of volunteer forces across the entire region. The activation of voluntary associations is carried out by the SOIR. The business activities of the volunteers associations are shared with CFD (Civil Protection) and are related to sensor monitoring tasks on the territory and collaboration during emergency situation.

3.6

Fire Brigade

The Fire Brigade from 2011 adopts the CAP Profile, a simple and flexible format for the exchange of digital data, open and non-proprietary, for the collection and distribution of notifications and emergency alerts. All Entities/Organizations will be equipped with instruments for standardized data exchange with the operating rooms.

4

Stakeholders interactions

Roles for emergency management

The entities and their roles involved in an emergency or crises event are different and heterogeneous. The envisaged flood emergency scenario involves multiple organizations having different roles and providing different services (e.g. police, medical care, rescue forces, fire fighters, etc.) and interacting vertically (i.e. with components of the same organization). Fig. 1 shows the institutions and actors involved in emergency situations and illustrates the role of the parties involved as described above. Analyzing this figure shows that the Prefecture has a central role. It is the party that coordinates the operations of emergency management. Furthermore, the Civil Protection plays a key role as it handles operations between SOIR and CFDs.

A CFD in particular retrieves important information like rainfall data directly from the sensor system within the region. Finally an increasingly important role is played by citizens. In fact, they become participants in a triple role: first, an active role to inform the organizations responsible when an emergency situation occurs; second, a passive role, which allows them to be informed in real time following the occurrence of an emergency situation in their area, and third, the possibility of being easily located on the territory and therefore being properly tasked or informed in case they are needed.

Actually most of the communication is done by phone or fax which of course is highly error prone in crisis situations and not state of the art anymore. Existing plans or organizational information is outdated or invalid this can quickly lead to confusion and wrong decisions. C2-SENSE system intends to overcome this unsatisfying situation and provide interoperable means (e.g. tools, adapters, applications) to the stakeholders involved in the Pilot Scenario.

Interoperability is important because it enables different components to work together across organizational and system boundaries. The ability representing the system as a set of components and to combine the various components is essential for the construction of complex systems. Without interoperability this becomes almost impossible -as in the myth of the Tower of Babel, where the inability to communicate (and interoperate) results in a systemic failure in the construction of the tower.

Thus, there is an urgent need to improve mechanisms for crisis management information and communication technologies that can keep track of the many details involved in all phases of disaster management. ICT enabled collaboration and related tools that support interoperability have become a must today for all task involved in emergency management.

The purpose of the C2-SENSE project is to ensure a proper management and coordination of all activities and tasks supported by a set of components and tools [START_REF] Schimak | EMERGENCY MAPS TOOL -facilitating collaboration and decision making during emergency & crises situations[END_REF]. The envisaged organizational structure for the scenario allows a seamless coordination of all technical and administrative activities respecting all the interest of the involved stakeholders (e.g. authorities, organizations, institutes, project partners, and external environments) in the project.

Fig. 1. Institutions and actors involved in emergency situations

Criticalities

As a result of a survey done at the beginning of the project several critical issues have been identified in relation to activities carried out by the Regional Civil Protection Department. They are mainly related to notification and communication management as well as to data and information sharing. This also relates to activities and processes of the alert management, situation monitoring and/or management of emergency meteorological, hydrological and hydraulic risks.

As already mentioned before, during an emergency situation, currently all information are managed through telephone communications (mainly), email, radio and fax machines that are simultaneously collected by involved stakeholders; which leads to huge challenge and considerable difficulties in managing all the diverse and heterogeneous information.

Therefore we put our focus in the pilot application respectively scenario on these issues and challenges in order to provide a better understanding of IT supported emergency management solutions.

5

Pilot Application

Functions

The Pilot Application respectively the Scenario has three main features to be tested: data sharing, notification management and communication management. Data Sharing as one of the important aspects to be tested. Key is the capability to share data and information about the actual situation but also about available resources with multiple applications and/or users.

In fact, to deal successfully with critical (crises/emergency) situations, it is crucial for all the involved systems (of different organization) to have all necessary information at their disposal. C2-SENSE has to guarantee to operate on an actual dataset that is coherent with the data provided by the involved organization. Thus, mutual interactions between organizations will imply information sharing between different systems. Consequently C2-SENSE will provide necessary data integration between different datasets in a transparent way with respect to the underlying technical systems and to their input specific sources.

Notification management, in any emergency situation, is one of the most important critical factors. Independent of the event (e.g. natural hazards, such as floods, tsunamis, etc. or from man-made accidents and threats) it is essential to be notified as fast as possible.

C2-SENSE shall provide a timely communication that is able to supply detailed and meaningful information, about an evolving emergency, to be exchanged among the involved institutional and non-institutional actors.

Communication management is one of the fundamental activities of emergency management. It becomes crucial when there are different subjects responding to an emergency and even more complex when we have to deal with different public information channels. Social media, especially Facebook and Twitter, but also WhatsApp, Telegram, YouTube, Instagram and Google Plus, are taking a very important part in the emergency response today. During emergencies, they are used by disaster responders, governments and non-governmental organizations, as an integrative information tool for crisis management.

Fig. 2. Pilot Application Functions

The involved stakeholders can be grouped into three categories: control base, on field points and command stations.

The control base is made up of civil protection systems which are considered the main user of the C2-SENSE system. As described in the previous paragraphs, it is divided into a Functional Center and a Control Room.

For field points we can find actuators such as alarms or automatic barrier systems, voluntary organizations occurring in the territory to meet the most varied events and emergencies, and citizens that can interact with the C2-SENSE system either to communicate an emergency situation or to receive information related to the evolution of the event and risk situations.

Command stations are command and control points such as prefectures and municipalities, but also others like the fire brigade and the medical centers involved in the territory where the emergency occurs.

Finally Fig. 2 shows the data inputs that enrich the system with information needed to manage the emergency. This information is introduced by sensors installed in the region, or is information shared by systems that are involved in emergency management.

C2-SENSE Pilot Application environment

In Fig. 3 the pilot environment is shown. It is divided into two different logical ecosystems: C2-SENSE test environment (on the top) and end-users systems (second half). C2-SENSE test environment is a digital ecosystem composed of various C2-SENSE tools having specific tasks: tools for definition and specialization of integration profiles, tools for communications management between the different C2-SENSE systems, and tools for performance monitoring. This ecosystem interacts with another ecosystem consisting of all systems of the local end users involved in emergency management and taking part in the pilot application testing. The interaction between the two ecosystems is done via different interfacing modules, called adapters, implemented between end-user's system and the C2-SENSE tools. Data used by the enduser system is translated by the adapter to make it C2-SENSE compliant; then, this information is processed and transmitted to the end-user recipient according to his local understanding or standards. The integration profiles define the way in which such integration has to be done.

Fig. 3. Pilot Application Environment

Pilot Scenario

C2-SENSE will assess its outcomes in a realistic pilot "Flood Scenario in Italy" to ensure that the developed profiling approach is generic enough but also applicable in real life situations [START_REF] Ciano | Profiling Approach for the Interoperability of Command & Control Systems with Sensing Systems in Emergency Management: an applicable scenario[END_REF]. The pilot territory chosen is located in the north of Apulia, and in particular in the Province of Foggia, forming the border with the region of Molise. The Pilot Scenario covers a period of two days and describes step by step what could happen, before and during a flooding along the Fortore River as well as the evolution of the event.

This area (Fig. 4) is characterized by the terrain of the Gargano promontory, and the flat plains of Tavoliere.

In this area, there are many rivers present, and for the trial river Fortore (Fig. 5) was selected. It crosses the territory from south to north, and collects the waters of four other rivers; The Occhito dam meets along the way, and flows into the Adriatic Sea, not far from Lake Lesina. • The National Weather Service (CFN) announces bad weather conditions for the next 24-36 hours; • The Regional Functional Center (CFD) issues a Bulletin about the regional criticality.

• The manager of the Regional Civil Protection publishes and sends an alert message to Prefectures, Municipalities, and other organization as far as they are concerned.

For the second day, the Pilot Scenario describes what happens during the flooding along the Fortore River, and the institutions and organizations involved including their roles and responsibilities. In particular:

• CFD (Regional Functional Center) follows the evolution of the situation through the regional monitoring network installed in the territory (monitoring and surveillance activities). • SOIR ensures emergency operations and H24 service to update the information related to the activities carried out, the type and amount of resources needed. • The Prefecture opens its Assistance Coordination Center (CCS) at the provincial level with the presence of Healthcare Service, Police Department, Fire Brigade, Red Cross, etc.

Municipalities, the responsible body in their territory, activate its own emergency procedures and keep in contact with the voluntary organizations and other involved organizations.

The events described in the Pilot Scenario are grouped into micro-scenarios (MS1-10 in following tables). They are used to describe in detail the interactions between two or more involved end-users. In particular: what is the event, which are the triggering systems, which are the target systems, what has to be tested, and what the positive/negative evaluation criteria are. In the following there is an overview about the proposed micro-scenarios used for the pilot:

Official sensor value display Fig. 6. Official sensor value display: pilot steps Decentralized Functional Centre (CFD) wants to show all the regional official sensors connected. They activate 'Emergency Map Tool (EMT)' [START_REF] Schimak | EMERGENCY MAPS TOOL -facilitating collaboration and decision making during emergency & crises situations[END_REF] and start the sensor data acquisition. The Emergency Map Tool acquires sensor data using the C2-SENSE system and displays them on the map. In this case all existing sensor networks are connected directly to C2-SENSE.

AdHoc Sensor adding Fig. 7. AdHoc Sensor adding: pilot steps Decentralized Functional Centre (CFD) decides to put additional sensor(s) in the field. They use the ActOnline1 (AOL) application to ask a volunteer organization to install (an) ad-hoc sensor(s) and activate them. ActOnline adapter catches this request and forwards it to the Enterprise Service Bus (ESB), further on another service catches it on ESB and forwards it to volunteers using TRBOnet2 application. Sensors are connected to the IP based gateway. They communicate with C2-SENSE using mobile phone network. Once activated, the sensor(s) immediately start sending data, and the data becomes visible in Emergency Map Tool.

COC Opening

Fig. 8. COC Opening: pilot steps

Operating Room of Civil Protection (SOIR) asks to the involved municipality to apply their Emergency Plan. This request will be performed using ActOnLine. The Message Communication Platform (MCP) will distribute the messages to the alert responsible persons in the municipality. At the same time the message will arrive via ActOnLine in the Municipality Emergency Control Room (COC) system. COC reads the message from ActOnLine and send a 'read notification' to SOIR using the C2-SENSE environment in order to acknowledge that the message was read by the COC.

Volunteers' involvement

Fig. 9. Volunteers' involvement: pilot steps

The volunteers of Foggia start the operation and activate their radio terminal. Then the radio terminal starts to send its GPS position using a specific TRBOnet function. The position is displayed on the Emergency Map Tool used in the Regional Civil Protection Operating Room (SOIR).

The Municipality Emergency Control Room (COC) requests the volunteer organization in order to start the foreseen operation as defined in the plan. The COC gives feedback to the Regional Civil Protection Operating Room (SOIR). SOIR reads the message and the notification of having read the message is sent to the COC.

The Regional Civil Protection Operating Room (SOIR) sends a message to a voluntary group and the Municipality Emergency Control Room (COC) in order to 'follow the situation on field' taking care about certain specific river points. The message is something like 'Attention, it is very important to follow the river 'abc' near the point 'xyz'. COC reads the message using ActOnLine which sends a 'read notification' to SOIR using the C2-SENSE environment. And also the volunteers' group receives the message directly on their radio device.

Risk detection

Fig. 10. Risk detection: pilot steps

Volunteers of Foggia report a dangerous flooding situation to the Municipality Emergency Operating Room (COC) using TRBOnet radio devices. The COC informs the SOIR (Regional Civil Protection Operating Room about the evolving situation (ActOnLine of COC sends a reply message to SOIR). SOIR reads the message and using its ActOnLine (AOL) that sends a 'read notification' to the COC. Under the responsibility of the Regional President of Regione Puglia the SOIR (Regional Civil Protection Operating Room) needs to alert the population via different channels using the Message Communication Platform (MCP): SMS, email, Twitter, Facebook (municipality page). SOIR prepares a message on ActOnLine (AOL) and the C2-SENSE system pushes the messages through the different channels, using a combination of the MCP, twitter and Facebook. The Social Network Adapters produce a reply message to inform the SOIR that the message was correctly delivered to Facebook and/or twitter. The Municipality Emergency Control Room (COC) requests healthcare services and equipment to provide first aid to the citizen. They ask this request to CSS (prefecture) and SOIR (Regional Civil Protection Operating Room) using its ActOnLine (AOL). C2-SENSE delivers the request to CSS by email (read notify is required) and to SOIR with ActOnLine. SOIR takes the request into account and sends 'mobile column' to the municipality. When COC receives the equipment it communicates the delivery of the 'mobile column' using ActOnLine (the message will be delivered to the SOIR and the Prefecture hat use ActOnLine and email).

Internal Civil Protection communication

Unique emergency number involvement

Fire Brigade involvement

Fig. 15. Fire Brigade involvement: pilot steps Municipality discovers a fire accident at a big fuel station. So they send a request to the Fire Department and they accomplish the request. During their intervention SOIR (Regional Civil Protection Operating Room) is updated continuously (C2-SENSE is involved to allow message exchange from/to fire department using CAP (Common Alerting Protocol) protocol and to update SOIR on via the ActOnLine system). SOIR operator reads the message and ActOnLine with C2-SENSE is used to provide a notification to COC (Municipality Emergency Operating Room).

Pilot test

Test phases and procedures

At the end of the deployment phase of the Pilot Application, the C2-SENSE system will be tested in order to demonstrate the effectiveness and feasibility of the project.

In this test phase the main components of C2-SENSE and interfacing with the local end-users systems will be tested. The test procedure is divided into three phases: Phase 0, Phase 1 and Phase 2.

In phase 0 the C2-SENSE system is configured and made ready to be used in an emergency situation. In this phase, the Emergency Interoperability Profiles will be created. As stated above the Emergency Interoperability Profiles will address all the layers of interoperability stack, i.e. physical layer, protocol layer, data/object model layer, information layer, knowledge layer. In this phase all profiles created for the different layer are generic, meaning that they are not specific to any country, organization or incident.

Phase 1 is located between before and during emergency situations. It can be regarded as a transition phase. In this phase, the generic Emergency Interoperability Profiles will be specialized for the Apulia region according to organizational structure and emergency procedures of this region. By using the Profile Specialization Tool, it will be possible to specialize and combine the profiles accordingly. To be more specific, they will be able to illustrate the Pilot Scenario through the GUI of Profile Specialization Tool and assign organizations, emergency teams, and emergency systems as actors of the scenario. By doing so, they will enable the execution and monitoring of the Pilot Scenario by the C2-SENSE system.

In phase 2 the C2-SENSE system is used in a real life emergency situation, e.g. flood in Apulia region. The profiles specialized for Apulia region will be executed through Profile Execution Engine. Execution of the specialized profiles means that organizations taking part in the emergency plan of Apulia region will exchange information among themselves according to the specifications in the profiles. C2-SENSE system will control, monitor and track these operations and display the progress through Profile Monitoring Tool.

Fig. 4 .

 4 Fig. 4. Test Area

Fig. 11 .Fig. 13 .

 1113 Fig. 11. Internal Civil Protection communication: pilot steps

Fig. 14 .

 14 Fig. 14. Unique emergency number involvement: pilot steps

ActOnline is a tool used for events and communications management during an emergency event. It is used by Civil Protection and municipalities.

TRBOnet is a tool installed on radio device used by volunteers in the Province of Foggia.

Acknowledgements

The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement nr. 607729.

Summary

This paper described applicative aspects related to the project C2-SENSE, with the aim of providing valuable and assessable instruments with regards to effective emer-gency management and interoperability among the information systems of the involved stakeholders.

In order to ensure that the developed profiles are appropriate in real life, they are being assessed in a realistic flood scenario in Apulia region of Italy.

The Pilot Scenario represents an attempt to apply the C2-SENSE system to a real situation in which the involved stakeholders will use IT technology to enable them to interact and communicate in way they could not do before. Furthermore the designed Pilot Scenario, demonstrates interoperability between the regional civil protection system and government agencies (Prefecture, Province, Municipalities), the other organizations like the fire brigade, the police, the medical rescue, and last but not least the citizens. Therefore the functional tests and results of different micro-scenarios presented in this paper shows that it is possible to adapt specific information technologies to a real emergency scenario, improving the interaction between the different organization and optimizing the response time during emergencies.


B Arbab-Zavar

L Middleton

N Museux

Z Sabeur

Z Zlatev

Large Scale Surveillance, Detection and Alerts Information Management System for Critical Infrastructure

Keywords: Big data, data fusion, Information systems, surveillance of critical infrastructure

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The SDAIM functional objectives specialize in the enablement of the intelligent fusion of data and information processing and reasoning from heterogeneous observation data sources. These are generated from a high variety of sensor observation platforms and processing components. Currently, the SDAIM considers observation data sources from 3D accelerometers, underground acoustic sensors, CCTV cameras, thermal and hyperspectral cameras, radars, SCADA (Supervisory Control and Data Acquisition) systems and human observers. This is achieved by undertaking sources, data and information modelling through the creation of metadata for such sources, in order to automate the system and data processing configurations. The aim is to achieve sensors platforms "plug-and-play" and the automatic on-demand access to fusion processes, their configuration and execution. Furthermore, open standards are used for fetching sensor data and processing metadata such as OGC Sensor Web Enablement using SensorML, JSON and RDF; and OWL for metadata description and modelling.

In addition to the above, the SDAIM system encounters high big data velocities for processing under the fusion resources. This is the case because of high data transmission volumes from thousands of sensors mounted at various platforms. This challenging problem is addressed through combining of advanced system's architecture design solutions with the adoption of specialized Big Data technologies. Specifically, the SDAIM advanced architecture is based on the de-facto functional data and information fusion model, the JDL/DFIG generic model [START_REF] Lambert | A blueprint for higher level fusion systems[END_REF], [START_REF] Sabeur | Structured Multi-level Data Fusion and Modelling of Heterogeneous En-vironmentalData for Future Internet Applications[END_REF], [START_REF] Zlatev | Agile data fusion and knowledge base architecture for critical decision support[END_REF]. This modelling framework provides highly structured functionalities of the SDAIM data processing modules, while adopting the current Google approach for scalable big data processing workloads. The latter uses concepts of Dockers for creating lightweight virtualized containers for the processing modules, and Kubernetes for scalable deployment and execution of processing components. This approach enables the SDAIM to be deployed and executed in a distributed fashion over a high variety of operating systems, ranging from lightweight embedded platforms to heavy-duty servers.

SDAIM Scalable Data Fusion Approaches

An important aspect with regards to the scaling of the SDAIM data fusion and processing is our structured approach for achieving a scalable high level data and information fusion. This particularly needs to be addressed at the JDL fusion level 3 on Situation Assessment. In the SDAIM, level 3 fusion components generate, what we call , 'alerts' and these needs to be critically achieved through an intelligent processing of big data for messaging them into a distributed system with effective action and mitigation on detected illicit activities at wide-zones. The JDL framework, see Fig. 1, is defined as follows 1 :

1 At this stage, only the first four levels of the JDL/DFIG framework are described. The full extended level will be described in the full paper as defined in Fig. 1.

Level 0  Fusion components generate signals, e.g. raw signal, co-registered signals with early data pre-processing, harmonisation and aggregations Level 1  Fusion components which specialise in the identification of background low level feature within a scene of interest Level 2  Fusion components which detect events concerning objects of targeted types ; and/or critical state transitions within a given sensory signal; and/or specific behaviours which may be qualified as unusual Level 3  Fusion components which generate alerts on the detected objects, events or behaviour of critical status that may compromise the security and safety of the widezone of interest. These fusion components work on the level of reasoning on objects and behaviours relations.

Alerts are consequently disseminated for consideration for decision-support to the security practitioners' operations via an online accessible graphical user interface. It will represent the Common Operational Picture (COP) of the wide-zone specific scene of interest. Depending on the COP diagnosis, the security practitioners could potentially raise a level of alert to a specified hierarchy of decision-makers and first responders. In it, the central components are the data and information fusion algorithms. These components, as per the JDL/DFIG model, fulfil fusion functions at different logical levels, e.g. signal processing, event or object detection, multiple classifications with various levels of confidence to high level extracted knowledge through fusion and reasoning on the wide-zone operational spatial and temporal contexts.

Finally, Fig. 3 depicts a simplified deployment case of the SDAIM. The SDAIM is deployed for a Wide-zone with 3 sub-regions and their respective sub-regional and global regional control centers.

Fig. 2. SDAIM architecture logical view

Fig. 3. SDAIM deployment for a Wide-zone with 3 sub-regions and sub-regional and a glob-al regional control centres

Applications on Surveillance, Detection and Alerts of Illicit Behaviour in Wide-Zones

In this section, we present early specific applications concerning surveillance, automated detection and alerts of illicit behaviour in wide-zones. The use of multiple type of sensing methods enable the automated detection and interpretation of potential illicit events occurring at wide zones. These approaches scale to large geospatial coverages and potential enable reasoning on detected events accordingly for establishing advanced situation awareness for safety and security management practitioners.

Physical Disturbance Event Detections at Fences of Critical Areas

Perimeter fences are widely used to protect Critical Areas such as water treatment plants, oil refinery, construction sites etc. Fence structures help to prevent only part of potential intrusions or postpone them. Therefore a high level of security is needed to monitor and investigate activities on and around fences. Accelerometers are relatively reliable tools which can be used for monitoring non rigid fences. While monitoring perimeter fences, two problems have to be addressed 1) Detect unusual events; and 2) classify these events to help with decision making and security related actions. Below, we present solutions to both of these problems using vibration sensors which are mounted on fences.

To efficiently detect unusual events along a perimeter fence, the developed event detection algorithm has to have the following properties: Fast, simple, and online; little or no interference of the user; data passed in small packets (1 or 2s of data); training stage enabled. Taking into consideration such requirements, an event detector based on Median Absolute Deviation (MAD) of signal and confidence interval method was adopted [START_REF] Barat D. Grishin | Detection of AE signals against background fric tion[END_REF]. For each sensor directional axis, y(N) will be a packet of data with specified window size N pass. The Median Absolute Deviation is a robust measure of data variability that can be calculated as follows:

Then the median of this packet of data should be inside a confidence interval with a selected value range. The lower and upper bounds of the confidence interval are calculated as:

( ) ( ) ( ) ( ) ( ) ( ) y up y low N y median N D N y median N D σ γ σ γ * + = * - = ,
Where, the standard deviation of the signal in the given window is estimated as γ was selected to be equal to 4 in order to guarantee more than 99.7% of confidence in the samples to be within these bounds.

Then an event for a given axis is detected if D(N)up-D(N)low>threshold. The latter is estimated using packets of data when no-activities take place. The threshold indicates an allowed deviation from the confidence interval when the packet of data will be considered while associated to no-activities. The quality of the MAD event detector is assessed using precision and recall measures. It was shown in the literature that such events as rattle, kick, climb or lean can be successfully classified for detecting security

( ) ( ) ( ) ( ) ( ) N y median N y median N y MAD - = ( ) ( ) 2 6745 . 0 * = N y MAD y σ
fence breaching under certain conditions [START_REF] Yousefi | Application of non-homogeneous HMM on detecting security fence breaching[END_REF]. In this paper, we will classify kick (K), shake (S) and no-activity (NA) events for each packet of data using a Bagging algorithm (Bag of decision trees) [START_REF] Meinshausen | Quantile Regression Forests[END_REF]. Cascade classification is also suggested in this paper. At the first stage, a packet of data is classified as Activity (A), No-activity (NA), Start (St) and End (E). If Activity (A) was classified, then this packet is classified as either kick (K) or shake (S). If the classifier returns Start event, then it is classified as a transition from no-activity to kick (NAK) or shake (NAS). The End event is classified as transition from kick or shake to no-activity (KNA, SNA). The initial investigation showed that mis-classifications of K and S occur quite often during transition periods due to damping effects of the vibration signal. The quality of classification is assessed using Correct Classification Rate (CCR) for each state detection.

Experiments were performed using fence structure, as shown in Fig. 4. Each fence section 2m high and 3m wide. S1 and S2 indicate the locations of the vibration sensors.

Fig. 4. Schematic presentation of non-rigid fence and vibration sensors

Six tests were performed with 2 persons who kicked and shook various sections of the fence at different times. Overall, 30 kicks and 31 shakes were experimented and recorded. The sensors were left on the fence for 15 minutes to record no-activities which used to calculate a threshold for event detection (it was 0 in this case for both sensors). The sampling rate of sensors were at 100Hz, and packets of 200 samples (2s of data) was passed to the MAD event detector and classifier. The start and end of events were manually labelled with some bias for the end of event due to the damping effect of the signal. For the training stage of the detector and classifier, 70% data was used for training (21 kicks and 22 shakes) while the rest of the data was used for testing (9 kicks and 9 shakes). In this paper, we consider High Level Event detection, i.e. an event is detected if the alarm was raised at least for one packet of data when the event takes place. Such event is marked as TP (True Positive). If the MAD event detector sets the alarm when no events took place, such event was marked as FP (False Positive). False Negative (FN) is counted when a whole event was missed. Table 1, below, shows recall and precision for individual tests and overall. Although all events were detected, some FPs occur usually in the end of an event. Examples of MAD event detector performances are shown in Fig. 5. A Cascade classifier which was suggested earlier achieved correct classification rate (CCR) at 93.75% overall. The Confusion Matrix for all detected events is given in Fig. 6.

NA is almost always classified correctly, the mis-classification usually occur during transition from K/S to NA which is expected due the nature of the vibration signals.

NA is mis-classified as transition NAS sometimes when a packet of data contains more than 60% of NA samples. CCRs for both K and S exceed rates of 85%. The results above showed that MAD event detector can be used to detect event reliably in Critical areas fences using vibration sensors, while the Cascade classifier can identify the nature of events taking place for further decision-support.

Unusual Behaviour Detection at a Toll Motorway

Automatic detection of incidents and unusual traffic events in motorways from visible spectrum videos is a challenging problem. These incidents range from: traffic collisions between vehicles and between vehicles and road structures, cars driving in the opposite direction or reversing, pedestrians and animals crossing the motorway, and more. Two main approaches are considered for this problem. First approach is through learning the environment and thus learning which motions and behaviours are usual in this environment [START_REF] Adam | Robust Real-Time Unusual Event Detection Using Multiple Fixed-Location Monitors[END_REF] [9] [10] [START_REF] Yun | Learning with Adaptive Rate for Online Detection of Unusual Appearance[END_REF]. The second possibility is the direct approach of de-tecting the specific objects and further detecting their motion and appearance and finally classifying these in terms of behaviours and events. Works on object classification and tracking [START_REF] Shah | Automated visual surveillance in realistic scenarios[END_REF], human detection and tracking [START_REF] Dalal | Histograms of Oriented Gradients for Human Detection[END_REF] [14] [START_REF] Niebles | Efficient Extraction of Human Motion Volumes by Tracking[END_REF] and human behaviour recognition [START_REF] Chaquet | A survey of video datasets for human action and activity recognition[END_REF] falls into this category.

Given the diversity of incident types, we have initially opted for the more generic approach of learning the usual behaviour/motions of the scene. Please note that this choice is often a trade-off between the flexibility and accuracy of the detector. Furthermore, the number of training examples are often limited and this would hinder the design of specialized detectors. A hybrid approach has since been developed to handle a specific case of stationary cars in a tunnel and a more generic detector for the nonroofed areas.

Detecting Unusual Behaviour via Learning the Usual Flow.

The approach here is similar to the method introduced by Adam et al. [START_REF] Adam | Robust Real-Time Unusual Event Detection Using Multiple Fixed-Location Monitors[END_REF] where a grid of local monitors learn the low-level local statistics of physical motions. A monitor will produce a local alert if the observed motion does not conform with the usual patterns of motion in that neighbourhood. These alerts are then fused across spatio-temporal windows to make the decision regarding the existence of an unusual event. The hypothesis states that incidents are events that disrupt the usual traffic flow in a motorway; and therefore can be detected as samples that do not fit the modelled usual flow. Fig. 7 summarizes this method. Two examples of detected unusual behaviour are also shown, where the area with unusualness has been highlighted automatically. These two examples show a car driving in opposite direction and a dog crossing the motorway.

Detecting Stationary Vehicles in a Tunnel.

The specific problem considered here concerns the detection of a stationary car and a pedestrian on the pavement in a tunnel while the traffic is in a one-way flow. The placement of the camera is such that the images of the vehicles are captured from a side/frontal view. It was found that the accuracy of detection using the above method is low due to some inherent difficulties of the set. These difficulties include: i) The specific pose and car headlights, which give rise to a significant amount of erroneous motion detections using optical flow; ii) Motion of cars in the left lane of the road are near to parallel to the camera's principal axis, due to direction of travel and the placement of the camera.

Fig. 7. Unusualness detection on traffic flow in motorways

As a result, the optical flow values of the stationary car do not produce the required signal to noise ratio for detection. A combination of background subtraction methods and a blob tracker is used to detect the stationary car and the person on the pavement. In this, the temporal variance-based method introduced by Joo and Zheng [START_REF] Joo | A temporal variance-based moving target detector[END_REF] and the median background subtraction are combined to obtain the robustness of temporal variance and capability of the median model to detect stationary objects. Further morphological transforms are used to clean the foreground, in order to assist the detection of distinctive blobs in the foreground. The detected foreground blobs are compared between two consecutive frames based on the size and motion of the blobs using a Kalman filter. The outcome of tracking is shown in Fig. 8. In December 2016, a prototype demonstrator was implemented on-site at an ATTIKES toll motorway, Athens, Greece. This prototype consisted of the major elements with reliable messaging using RabbitMQ and algorithm processing codes which were implemented within linux containers (Docker). The processing was then passed to a reasoning engine which performed event stream processing and high level fusion to make final assertions about the sensor processing and detected events in real-time. Several days of testing were performed along with a final live exercise evaluated by end-users. The use of containers made fixing and redeploying the detection algorithms very efficient. The aim was for the Motorway Traffic Management Centre at Attikes to be enabled with automated detection of unusual events in their motorways sections. With tens of surveillance cameras in place at the Centre, staff cannot efficiently detect all unusual events with rapid response. The SDAIM consequently provides real-time alerts to events for staff which will reduce their response time lags by 50% according to the discussion which we conducted with Attikes staff. Additionally, and due to the flexible nature of the SDAIM architecture, we will deploy further performing algorithms for unusual event detection. The final version of the SDAIM system will be tested and validated at the Traffic Management Centre for Attikes Motorway in May 2018.

5

Fig. 1 .

 1 Fig. 1. The JDL/DFIG Data and Information Fusion model

Fig. 5 .Fig. 6 .

 56 Fig. 5. Performance of the MAD event detector: The best and average examples

Fig. 8 .

 8 Fig. 8. Blob tracking based on foreground detection

Table 1 .

 1 Performance evaluation of MAD event detector

	Test	Test1 Test2	Test3	Test4	Test5	Test6
	Recall	100% 100%	100%	100%	100%	100%
	Precision 93.3% 72.7%	100%	84.2%	88.9%	90%

Acknowledgement

The authors would like to thank partners TEKNIKER and Attikes for the acquisition of experimental data using accelerometers and CCTV respectively. The ZoneSEC research project is partly funded by the European Union under contract Number: EC_FP7 607292.


Asaf Nebenzal
email: asaf.n@technion.ac.il

Barak Fishbain
email: fishbain@technion.ac.il

Hough-Transform-Based Interpolation Scheme for Generating Accurate Dense Spatial Maps of Air Pollutants from Sparse Sensing

Keywords: Air-quality modeling, Spatial maps, Interpolation, Hough transform, Source detection

Air pollution is a significant health risk factor and causes many negative effects on the environment. Thus, arises the need for studying and assessing air-quality. Today, air-pollution assessment is mostly based on data acquired from Air Quality Monitoring (AQM) stations. These AQM stations provide continuous measurements and considered to be accurate; however, they are expensive to build and operate, thus scattered sparingly. To cope with this limitation, typically, the information obtained from those measurements is generalized with interpolation methods such as IDW or Kriging. Yet, the mathematical basis of those schemes defines that pollution extremum values are obtained at the measuring points. In addition, they are not considering the location of the pollution source or any physicochemical characteristics of pollutant hence do not reveal the real spatial air-pollution patterns. This research introduces a new interpolation scheme which breaks the interpolation process into two stages. At the first stage, the source of pollution and its estimated emission rate are inferred through a detection procedure which is based on the Hough Transform. At the second stage, based on the detected source location and emission, spatial dense pollution maps are created. The method requires, for its computation, to assume a dispersion model. To this end, any model can be used as sophisticated as it may be. Spatial maps created with simplified dispersion models in a computational simulation, show that the suggested interpolation scheme manages to create more accurate and more physically reasonable maps than the state-of-the-art.

Introduction

Air pollution is a significant risk factor for multiple health situations including eye irritation, breathing difficulties, lung cancer, heart diseases and respiratory infections [START_REF] Heroux | Quantifying the health impacts of ambient air pollutants: recommendations of a WHO/Europe project[END_REF].

In addition, air-pollution causes many negative effects on the environment like decreased visibility, acid rain, global warming, climate change, water quality deterioration and ecosystems destruction [START_REF] Venkatadri | A survey on Air Quality forecasting Techniques[END_REF]. Thus, arises the need for studying and assessing air-quality's characteristics, dispersion patterns and behavior. Today, numerous air-pollution studies are based on data acquired from Air Quality Monitoring (AQM) Stations [START_REF] Özkaynak | Air pollution exposure prediction approaches used in air pollution epidemiology studies[END_REF]. However, AQM are typically scattered sparingly, mainly near main roads, industrial factories, or near highly populated areas [START_REF] Goswami | Spatial characteristics of fine particulate matter: identifying representative monitoring locations in Seattle[END_REF]. Thus, the AQM network has a limited ability to account for spatial variability of pollution levels in heterogeneous regions, such as urban areas, which in return, renders exposure assessment as a difficult task [START_REF] Rao | Environmental Modeling and Methods for Estimation of the Global Health Impacts of Air Pollution[END_REF]. To cope with the measurements sparsity, the information obtained from those measurements is often generalized with mathematical methods to improve the spatio-temporal coverage. To this end, interpolation schemes are sought.

Interpolation is a mathematical method of constructing a continuous function that obtains the measured values (or close values) at the measuring point. Environmental interpolation is based on the assumption that data attributes are continuous over space and spatially dependent [START_REF] Akkala | Interpolation techniques and associated software for environmental data[END_REF]. Grossly speaking, interpolation methods can be divided into deterministic and geostatistical methods. The first include Inverse Distance Weighted (IDW), Nearest Neighbor (NN) and radial basis functions [START_REF] Akkala | Interpolation techniques and associated software for environmental data[END_REF], while the latter involve, for example, various types of Kriging methods [START_REF] Li | A review of spatial interpolation methods for environmental scientists[END_REF]. Next, we focus on IDW and ordinary Kriging, owing to their frequent use in spatial maps creation.

There are many studies in the field of air pollution modeling that used IDW or Kriging for creating dense spatial map of air pollution. IDW, for example, was applied for examining the ratio between low birth weight and air pollution exposure during pregnancy [START_REF] Xu | PM10 air pollution exposure during pregnancy and term low birth weight in Allegheny County, PA, 1994-2000[END_REF]. In that research, the IDW interpolation was utilized for estimating PM10 levels at future mothers' home address. Clark et. al [START_REF] Clark | Effect of early life exposure to air pollution on development of childhood asthma[END_REF] examined the effect of early life exposure to air pollution on development of childhood asthma. For estimating the average exposure level of an area, IDW interpolation was applied. Ventura et. al [START_REF] Trujillo-Ventura | Multiobjective air pollution monitoring network design[END_REF] introduced multi-objective pollutant AQMs optimization. In their research, they applied a Kriging interpolation scheme for creating dense spatial maps. Sarigiannis and Saisana [START_REF] Sarigiannis | Multi-objective optimization of air quality monitoring[END_REF] used Kriging interpolation method to create pollution maps of CO and O3 as an additional input to their multi-objective optimization scheme, which was based on remote sensing satellites.

IDW and Ordinary Kriging are both well-known and widely used interpolation methods. However, these methods are not appropriate for creating air-pollution spatial dense maps for several reasons: The mathematical basis of those schemes defines that all interpolated values over the study area are essentially a weighted average of the measurements points, thus extremum values cannot be obtained at any other place than the measuring points. In addition, these methods do not consider the location of pollution sources or any physicochemical characteristics of pollution. Hence, the resulted dense pollution maps do fall short in describing accurately the real spatial patterns of pollution. Regarding these in the interpolation process is expected to result in better and more accurate interpolation methods.

This research introduces a Hough Transform-Based Interpolation (HTBI) method, which generates accurate dense pollution maps through finding sources' locations and the utilization of an air pollution dispersion model. The Hough Transform is a mathematical method, originated in image processing, used for detecting geometric shapes, like lines, circles or ellipses [START_REF] Hough | Method and means for recognizing complex patterns[END_REF]. The main idea is converting from representing the shape in x,y coordinates (Cartesian) system to a parametric space, where the feature of interest is best represented. In this research, a feature space, which will represent best the source location is devised.

The method consists of two phases: at first, based on ambient concentration and assuming a dispersion model, the HTBI detects the sources' emission rates and locations. Then, using this information, the interpolation scheme builds the continuous pollution field. The suggested HTBI scheme applies no constraint on the assumed dispersion model. Hence, any dispersion model found in the literature (e.g., [START_REF] Hystad | Creating national air pollution models for population exposure assessment in Canada[END_REF]- [START_REF] Tominaga | CFD simulation of near-field pollutant dispersion in the urban environment: A review of current modeling techniques[END_REF]), as sophisticated as it may be, can be incorporated into the suggested scheme.

Methodology

Notation

The following notation facilitate the description of the method. Let {𝑆𝑆} be a set of sources of a specific pollutant, with emission rates {𝑄𝑄}. Let 𝐴𝐴 be the specific pollutant's continues signal generated by {𝑆𝑆}, defined over a geographical area Ω. {𝑆𝑆} are located at {𝛾𝛾}𝜖𝜖Ω. Let {𝑎𝑎} be a finite set of samples of signal 𝐴𝐴, taken in locations {𝜔𝜔} ⊂ 𝛺𝛺.

Interpolation aims at estimating 𝐴𝐴 over the entire space Ω, based on the set of samples {𝑎𝑎}. This is achieved here by first finding sources' locations, {𝛾𝛾}. It is worthwhile noting that the discussion here is limited to a single pollutant interpolation, i.e., the generation of a dense map of the specific pollutant is based on a set of sparse measurements of the same pollutant.

Interpolation Scheme

Each sample 𝑎𝑎 𝑖𝑖 ∈ {𝑎𝑎}, represents a measurement in 𝜔𝜔 𝑖𝑖 . W.L.O.G, if we order {𝑆𝑆}, and {𝑄𝑄} is sorted so 𝑄𝑄 𝑖𝑖 is the emission rate of source 𝑆𝑆 𝑖𝑖 ; 𝑎𝑎 𝑖𝑖 is a weighted combination of the contributions from all the sources, 𝑄𝑄 ��⃗ . Assuming a dispersion model, 𝑀𝑀, so the k th element of the vector 𝑀𝑀 𝚤𝚤 ����⃗ is the decay coefficient of source k, 𝑄𝑄 𝑘𝑘 , in location i; sensor i's measurement, 𝑎𝑎 𝑖𝑖 , is all sources contributions at i and is given by:

𝑎𝑎 𝑖𝑖 = 𝑀𝑀 𝚤𝚤 ����⃗ • 𝑄𝑄 ��⃗ 𝑇𝑇 (1)
Consequentially, forming the set {𝑎𝑎} as a vector, all sensors' measurements can be represented by the following matrices multiplication:

𝑎𝑎 ⃗ = 𝑀𝑀 • 𝑄𝑄 ��⃗ 𝑇𝑇 (2)
Given [𝑀𝑀], we assume that there exists a matrix 𝐸𝐸, which satisfies:

𝑄𝑄 ��⃗ = [𝐸𝐸] • 𝑎𝑎 ⃗ 𝑇𝑇 (3)
For finding 𝑄𝑄 and 𝛾𝛾, a search on the entire Ω is suggested. To this end, Ω is divided into 𝑁𝑁 disjoint catchments. We assume that each catchment, 𝐶𝐶 𝑛𝑛 ⊆ Ω is small enough so the pollution is uniform all over it. For each of the catchments an estimated emission rate 𝑄𝑄 � 𝑖𝑖 𝑛𝑛 is calculated, based on accepted measurements from single sample 𝑎𝑎 𝑖𝑖 ; where 𝑒𝑒 is a single row of 𝐸𝐸:

𝑄𝑄 � 𝑛𝑛 𝑖𝑖 = 𝑒𝑒 • 𝑎𝑎 𝑖𝑖 (4)
Thus, 𝑄𝑄 � 𝑖𝑖 𝑛𝑛 introduces the estimated emission rate from the single source 𝑆𝑆, had it was located at 𝐶𝐶 𝑛𝑛 , based on the single measured sample at 𝑎𝑎 𝑖𝑖 . The same process is applied for all 𝐶𝐶 𝑛𝑛 for each of the sensors:

𝑄𝑄 � �⃗ 𝑛𝑛 = [𝐸𝐸] • 𝑎𝑎 ⃗ 𝑇𝑇 (5)
Applying Equation ( 5), results in each 𝐶𝐶 𝑛𝑛 having its unique set of 𝑄𝑄 � �⃗ 𝑛𝑛 , one estimate for each sensor. Using the standard deviation (STD) of the estimates, the catchment with the lowest STD is the approximated location of 𝑆𝑆. Once the source location, 𝛾𝛾, is obtained, the emission rate of 𝑆𝑆 is estimated by the average of the catchment's estimates:

𝑄𝑄 � 𝛾𝛾 = 𝑄𝑄 � �⃗ � 𝛾𝛾 (6)
Having the estimated emission rate 𝑄𝑄, of the source 𝑆𝑆, and its estimated location, 𝛾𝛾, with the dispersion model 𝑀𝑀, we can now estimate the dense pollution map over Ω:

𝐶𝐶 𝑛𝑛 = 𝑀𝑀 ��⃗ • 𝑄𝑄 � (7)
The process is illustrated in the simple example of Fig. 1, where three sensors are deployed in a region with one source (see Fig. 1a). While the catchments can assume any geographical region and shape, for the sake of simplicity, the region, Ω, is divided into squared catchments, forming a squared grid. Sensor 1, which measures a pollution level of 33 (i.e. 𝑎𝑎 1 = 33), is located at catchment (1,3); Sensor 2, located at (2,4), measures 29; and Sensor 3, at (3,3) measures a level of 30. Keeping in mind the source's location, 𝛾𝛾, is unknown, Fig. 1b demonstrates the execution of Equation ( 4), where each catchment is assigned with the estimated source's emission rate if the source was located in this catchment, given Sensor 1's measurement, and an exponential isotropic decay dispersion model, with an extinction coefficient 𝜆𝜆. I.e., for 𝑟𝑟, the Cartesian distance from the source, the pollution level at each location on the map is given by [START_REF] Buhmann | Radial basis functions: theory and implementations[END_REF]:

𝑎𝑎 𝑖𝑖 = 𝑄𝑄 • 𝑒𝑒 -𝜆𝜆|𝑟𝑟| (8)
If the source was located at (2,2), then the estimated emission rate, 𝑄𝑄 � , based on Sensor 1's measurement, should have been 38. If the source was located at (1,4), then 𝑄𝑄 � , according to Sensor 1, would be 47.3. Fig. 1c and Fig. 1d are the estimation maps, generated in the same fashion as b, for Sensor 2 and Sensor 3 respectively. Assuming the dense pollution maps are a collection of isolines, the estimated emission rate values of the three sensors should agree in one grid location [START_REF] Ballard | Generalizing the Hough transform to detect arbitrary shapes[END_REF]. To evaluate the agreement, we compute, in each 𝐶𝐶 𝑛𝑛 , the three sensors' estimates' standard deviation. The lower the STD, the higher the agreement. This is illustrated in Fig. 1e. The smallest STD, indeed is obtained at location (1, 1), where, in this example the source is located.

Results and Discussion

Dispersion Models

As mentioned earlier, 𝑀𝑀 represents the pollution decay function of the dispersion model. The suggested scheme, HTBI, does not apply any constraint on the dispersion model used. It can be any model, as long as it allows to compute the expected pollution on any given location on the map, given the emission rate 𝑄𝑄 and all other meteorological parameters required by the specific model in use. In this research, two models were used, the above isotropic decay dispersion model [START_REF] Buhmann | Radial basis functions: theory and implementations[END_REF] (Equation ( 8)) , and the wellknown Gaussian Plume Dispersion (GPD) model [START_REF] Ermak | An analytical model for air pollutant transport and deposition from a point source[END_REF]:

𝑎𝑎 𝑖𝑖 (x, y, z) = 𝑄𝑄 2𝜋𝜋𝜎𝜎 𝑦𝑦 𝜎𝜎 𝑧𝑧 𝑢𝑢 � exp �- 𝑦𝑦 2 2𝜎𝜎 𝑦𝑦 2 � • �exp(- (𝑧𝑧 -𝐻𝐻) 2 2𝜎𝜎 𝑧𝑧 2 ) + exp(- (𝑧𝑧 + 𝐻𝐻) 2 2𝜎𝜎 𝑧𝑧 2 )� (9)
Where x is the downwind, y is the crosswind and z is the vertical distances of 𝑎𝑎 𝑖𝑖 from the source; 𝑢𝑢 � is the time-averaged wind speed at the hight of release H; and σy and σz represent the standard deviations of the crosswind and vertical Gaussian distribution of the pollutant concentration, respectively. The model also assumes full reflection from the ground.

Computational Simulation

For generating a continuous pollution field, the two types of dispersion models, described above were used. Specifications of the models are: 𝑄𝑄 =8 ton/hr; wind speed (for the GPD model):4 m/hr; wind direction (GPD model): 285°; effective stack-height: 120m.

The continuous fields were sampled by the set of sensors described in Fig. 2 . To simulate real conditions, additive white Gaussian noise with Signal to Noise Ratio (SNR) of 10% (10 dB) was added to the readings of the sensors. Each sensor is now reporting the ambient level in its location as derived from the dispersion model with noise added. See Table 1 for ambient data measured in each sensor, for the radial and the GPD models.

Using only the noisy readings obtained from the sensors, {𝑎𝑎}, the source's location is estimated and then the dense pollution maps are created. ( The results obtained for the radial dispersion model (Equation ( 8)) are displayed in Fig.

3.

The highest ambient pollution level is located at the source location and exponentially decay as moving away. However, both IDW and Kriging models create a pollution map in which the maximum pollution level is obtained at the closest sensor to the sources' location, and decay as the distance from the source decreases (Fig. 3 The suggested algorithm is deterministic in nature, i.e., for the same input, the system will produce the same output. Therefore, the uncertainty in the system stems from the uncertainty of the measurements, i.e., measurements noise [START_REF] Fishbain | An Evaluation Tool Kit of Air Quality Micro-Sensing Units[END_REF]- [START_REF] Moltchanov | On the feasibility of measuring urban air pollution by wireless distributed sensor networks[END_REF]. The results of Fig. 3 and Fig. 4 were obtained at a noise level of 10%. (SNR, of 10dB). For evaluating the robustness of the algorithm, different noise levels were tested with the system. The radial model (Equation ( 8)) showed stability even with up to 50% errors (SNR of 3dB).

The Gaussian model's (Equation ( 7)) robustness showed dependency on the catchments size. For larger catchment sizes (e.g., cell size of 40m 2 ), our algorithm showed stability up to 10% SNR. However, increasing the spatial resolution to a cell size of 20m 2 , the HTBI showed higher sensitivity to measurement noise and showed the correct source location and interpolation maps for noise levels of up to 5% (13 dB). For lower SNR values the algorithm faced difficulties in locating the source and consequentially generating the dense pollution maps.

Conclusions

IDW and Ordinary Kriging are well-known and commonly-used interpolation methods for creating dense spatial maps, however they are not considering the physicochemical properties of the pollution characteristics nor the source location, therefore not accurate for this task. In this research, we introduced the Hough Transform Based Interpolation (HTBI), a two-phase interpolation scheme, which addresses these limitations. At the first phase, the HTBI detects sources' locations and their estimated emission rate. Using this information, at the second phase, a dense pollution spatial map is built. The method incorporates an air-pollution dispersion model into its calculations. This may be any dispersion model that can be found in the literature. Comparing between the dense pollution maps created by the HTBI, IDW and Ordinary Kriging shows that the HTBI creates spatial maps, which represents the true pollution maps better and thus, is more accurate and sensible interpolation scheme. However, this work showed a computational simulation of a simple configuration with only one emission source. Implementing the method to a real-word situation is challenging. Air pollution emitted from many sources including industrial zones and transportation (line source). Hence, HTBI should be adjusted to face with this complex situation of multi sources detection. Despite the above, HTBI indeed can be used in its current form, a single source detection. We can imagine at least two scenarios in which such configuration applies. The first is indeed when a single source can be identified. For example, when considering SO2 which is emitted mainly from factories, and the study area contains only single industrial zone. The second is a case of leaks and we would like to identify the leak's source. In these cases, HTBI will be able to produce better and accurate spatial pollution maps than the existing methods. Current work, carried out these days, is focusing on the implementation of HTBI in exactly such scenarios.

Another aspect this work sheds light on is the number of sensors and the way they scattered in the study area. It is obvious that the higher the number of sensors, the easier it will be to locate the source. There is a need for further research in finding the optimal number of sensors in a given area. The parameters that should be considered are the size of the study area, the characters of it (an open area is not the same as crowded urban area.), the coverage capacity and accuracy of the sensors and more.

Fig. 1 .

 1 Fig. 1. Source location identification through HTBI assuming simple radial dispersion model.

Fig. 2 .

 2 Fig. 2. A 20 km 2 geographical area, Ω. The sensors' locations are marked with pink circles, with a serial number below and the source (which typically its location is unknown) is marked by yellow star. Wind direction represented by red arrow.

Fig. 3 . 4 .Fig. 4 .

 344 Fig. 3. Dense pollution maps based on the radial dispersion model: (a) IDW; (b) Ordinary Kriging; (c) HTBI. Sensors' locations are marked in pink and the source's location is marked with a yellow star. Pollution level is represented in a blue (low)-to red (high) color scale.The interpolation results for the GPD model are presents in Fig.4. As both IDW and Kriging do not consider physicochemical characteristics nor atmospheric conditions, the maximum of the dense pollution maps is found at the closest sensor downwind from the sources' location (Fig.4(a) and (b) respectively). Moreover, the created maps demonstrate a roughly radial dispersion around this point, which is not the true condition, due to the wind. HTBI, as presented in Fig.4(c), does manage to create a dense spatial map which complies with the Gaussian plume behavior. This is attributed to the fact that the HTBI method does incorporates the Gaussian model, as it can incorporate any dispersion model.

Table 1 .

 1 Ambient data measured by the sensors (units are in µg/m 3 ) for the radial and the GPD dispersion models

	Sensor #	Radial	GPD
	(1)	0.180	0
	(2)	2.586	0
	(3)	3.359*e-10	6.590*e-30
	(4)	1.606*e-16	282.5981
	(5)	1.659*e-20	6.20*e-17
	(6)	1.233*e-30	1.1097


Erechtchoukova

Peter A Khaiter
email: pkhaiter@yorku.ca

Marina G Erechtchoukova
email: marina@yorku.ca

Conceptual design of a software tool for management of biological invasion

Keywords: Invasive species, Ecosystem, Norway maple, Software tool, Functional architecture, Decision-making

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Introduction

Biological invasion of nonnative species is considered as one of the major threats to sustainable development and as a major danger to marine and terrestrial biodiversity (Molnar et al. [START_REF] Molnar | Assessing the global threat of invasive species to marine biodiversity[END_REF]; Hughes and Worland [START_REF] Hughes | Spatial distribution, habitat preference and colonization status of two alien terrestrial invertebrate species in Antarctica[END_REF]). Alien species are seen as one of the primary means for human-accelerated global change: they pose a threat to biodiversity, re-work ecosystem arrangements, tasks and services, and induce huge economic costs and serious health complications to humans (Mazza et al. [START_REF] Mazza | Biological invaders are threats to human health: an overview[END_REF]). The effects of having no control in place for invasion species could be costly in terms of both direct monetary values and the negative consequences for human life (Andersen et al. [START_REF] Andersen | Risk analysis for invasive species: general framework and research needs[END_REF]).

In the present study, we focus on the case of Norway maple (Acer platanoides). This plant species was introduced intentionally from continental Europe during the mid-1700s to eastern North America (initially to Philadelphia around 1760) as an ornamental shade tree and then widely planted during the latter half of 20th century (Webb et al. [START_REF] Webb | The myth of the resilient forest: case study of the invasive Norway maple (Acer platanoides)[END_REF]; Wangen and Webster [START_REF] Wangen | Potential for multiple lag phases during biotic invasions: reconstructing an invasion of the exotic tree Acer platanoides[END_REF]). Nowadays, it has invaded northeastern forests of the United States and the riparian and mesic montane forests of the northern Rocky Mountains (Reinhart et al. [START_REF] Reinhart | Invasion through quantitative effects: intense shade drives native decline and invasive success[END_REF]). A. platanoides has been recognized as a serious threat to native forest ecosystems in the United States and Canada.

The scales of invasive spread call for managerial actions aimed at the protection and restoration of native ecosystems (e.g., removal of A. platanoides from invaded areas, Webb et al. [START_REF] Webb | The myth of the resilient forest: case study of the invasive Norway maple (Acer platanoides)[END_REF]) which are associated with considerable difficulty and expense and whose effect is not easily foreseeable due to the complexity and substantial non-linearity of the contributing factors and processes. To facilitate the management efforts, decision-makers and environmental practitioners should be equipped with a software tool integrating relevant knowledge and acting as a supporting expert.

Methodology

Biological invasion is a complex phenomenon making decisions about management of invasive cases inherently difficult because of the multifactorial and multiattribute scope of the problem, a great level of uncertainty regarding the outcomes of possible management actions, multiple, sometimes conflicting, objectives and numerous parties involved in the process [START_REF] Maguire | What can decision analysis do for invasive species management?[END_REF]). In the sub-sections below, we discuss some important aspects of the problem.

Phases of invasion process

The process of plant invasion comprises certain stages (Fig. 1). Thus, Radosevich et al. [START_REF] Radosevich | Plant invasions: process and patterns[END_REF] defined three phases: introduction, colonization and naturalization. Wangen and Webster [START_REF] Wangen | Potential for multiple lag phases during biotic invasions: reconstructing an invasion of the exotic tree Acer platanoides[END_REF] paralleled them with the phases of stratified diffusion by Shigesada et al. [START_REF] Shigesada | Modelling stratified diffusion in biological invasion[END_REF]. Andersen et al. [START_REF] Andersen | Risk analysis for invasive species: general framework and research needs[END_REF] argued for four phases of entry, establishment, spread and impact. In the latter classification, the entry phase marks the arrival of a non-indigenous species into a new environment; the establishment phase occurs where this arriving population begins to reproduce in situ and escapes immediate danger of local extinction; in the spread phase, the species disperses from its initial site of establishment and occupies available habitat within its new environment; and in the impact phase, an established species persists and competes in its new geographical range (Andersen et al. [START_REF] Andersen | Risk analysis for invasive species: general framework and research needs[END_REF]).

Tree development stages

The starting point in the tree life history is seed planting (Fig. 2). Surviving seeds will germinate and establish the seedlings. Seedlings are defined as trees whose height does not exceed the maximum seedling height for a given species (e.g., 1.00 m, Wyckoff & Webb [START_REF] Wyckoff | Understory influence of the invasive Norway maple (Acer platanoides)[END_REF]; 1.35 m, Murphy [START_REF] Murphy | SORTIE-ND User Manual[END_REF]). Accordingly, seedlings have no diameter at breast height (DBH); their primary size measurement is the diameter at a height of 10 cm (diam10). When a seedling reaches the maximum seedling height, it becomes a sapling, for which a DBH is greater than 0 and less than the minimum adult DBH (Murphy [START_REF] Murphy | SORTIE-ND User Manual[END_REF]). The sampling phase continues until the tree reaches a specific threshold height (e.g., 3 m; Senbeta and Teketay [START_REF] Senbeta | Regeneration of indigenous tree species under the canopies of the tree plantations in central Ethiopia[END_REF]) or the minimum adult DBH (e.g., 2.5 cm, Wyckoff & Webb [START_REF] Wyckoff | Understory influence of the invasive Norway maple (Acer platanoides)[END_REF]), after which it becomes adult. Snags are standing dead trees which are produced when either saplings or adults die due to normal tree mortality or a disturbance event, such as disease. Fallen snags form woody debris.

Ecosystem scope

It is commonly accepted that alien species produce substantial negative effects on the composition, structure and functioning of the invaded ecosystems (e.g., Higgins et al. [START_REF] Higgins | Modeling invasive plant spread: the role of plant-environment interactions and model structure[END_REF]; Wangen and Webster [START_REF] Wangen | Potential for multiple lag phases during biotic invasions: reconstructing an invasion of the exotic tree Acer platanoides[END_REF]). Therefore, the ecosystem scope needs to be taken into consideration in the analysis for decision-making associated with invasive cases. The introduction of nonnative species is a stress onto invaded ecosystems, and this stress, in most of the cases, will be compounded with, and possibly amplified by, other natural and anthropogenic influences. The impacted ecosystem, its components and functions will react to stress in different ways. A typology of ecosystem stresses (sensu Khaiter and Erechtchoukova [START_REF] Khaiter | The notion of stability in mathematics, biology, ecology and environmental sustainability[END_REF]; Gutiérrez et al. [START_REF] Gutiérrez | Toward an integrated ecosystem perspective of invasive species impact[END_REF]) enables differentiation between specific categories of stress, on the one hand, and the distinct functions and ecosystem components (biotic and abiotic) being influenced, on the other. Furthermore, it is important for practical environmental management to predict the persistence capacity and probable transformations in invaded ecosystems. It has been demonstrated (Khaiter and Erechtchoukova [14]) that there are common patterns in the behaviour of ecosystems as they respond to exogenous disturbances, and the following five scenarios in ecosystem stress dynamics have been determined: (1) resistance; (2) deformation; (3) resilience; (4) degradation; and (5) shift. To predict a particular scenario, a good understanding of the impact mechanisms driving the changes is necessary, but by far, it remains rather limited (Reinhart et al. [START_REF] Reinhart | Invasion through quantitative effects: intense shade drives native decline and invasive success[END_REF]).

Driving mechanisms

From the ecosystem perspectives, persistence to invasion occurs in the form of competition from the native communities (Martin and Marks [START_REF] Martin | Intact forests provide only weak resistance to a shade-tolerant invasive Norway maple (Acer platanoides L.)[END_REF]), and a dominating concept since seminal paper by Elton [START_REF] Elton | The ecology of invasions by animals and plants[END_REF] has been that resistance to invasion is greater in intact or undisturbed communities. However, recent studies are not so definitely supportive of this paradigm (e.g., Webb et al. [START_REF] Webb | The myth of the resilient forest: case study of the invasive Norway maple (Acer platanoides)[END_REF]) and rather unveil a more complicated interplay of biotic and environmental drivers in the resulting ecosystem resistance to biological invasion. In addition, competition with resident species can take on multiple forms -e.g., in the cases of woody invasion in forest ecosystems, for light, soil nutrient resources, as allelopathic interference and disruption of mycorrhizal associations (Urgenson et al. [START_REF] Urgenson | Multiple competitive mechanisms underlie the effects of strong invader on early-to late-seral tree seedlings[END_REF]). However, invasive plant species may bring novel symbiotic mutualisms into the ecosystem (Vitousek et al. [START_REF] Vitousek | Biological invasion by Myrica faya alters ecosystem development in Hawaii[END_REF]). The resistance to invasion in forest ecosystems can be modified by environmental factors, such as soil moisture and nutrient levels (e.g., nitrogen, Walters and Reich [START_REF] Walters | Are shade tolerance, survival, and growth linked? Low light and nitrogen effects on hardwood seedlings[END_REF]) or soil pH. Strongly acidic soils offer the highest resistance to invasion, while base-rich soils can significantly reduce invasion resistance (Martin and Marks [START_REF] Martin | Intact forests provide only weak resistance to a shade-tolerant invasive Norway maple (Acer platanoides L.)[END_REF]). The outcome of this competition can affect critical functional roles in both terrestrial and adjacent aquatic habitats: regulating microclimate, stabilizing stream banks and water flow and providing energy and nutrients to soil and aquatic food webs (Urgenson et al. [START_REF] Urgenson | Multiple competitive mechanisms underlie the effects of strong invader on early-to late-seral tree seedlings[END_REF]), i.e., ecosystem services.

EMDMIC design

In this section, the development of a software tool for environmental modelling and decision making in managing of invasive cases (EMDMIC) is presented. Following its introduction (Khaiter et al. [START_REF] Khaiter | A Framework for Decision-Making in Cases of Invasive Species[END_REF]), architectural design and detailed aspects are considered in the paper. Though the motivation for the study has been the case of Acer platanoides, there are good reasons to believe that the suggested software tool is suitable for a broader range of biological invasions in forest ecosystems. The tool consists of the three main modules (Fig. 3): "Factors", "Ecosystem Modelling" and "Management" which are discussed in the corresponding sub-sections below. The "Factors" module specifies a particular stage of the invasion process (see sub-section 2.1) as well as environmental factors (e.g., topology, geology, soil composition, hydrology and meteorology, including the annual insolation above the forest canopy, Botkin et al. [START_REF] Botkin | Some ecological consequences of a computer model of forest growth[END_REF]) and biological factors (e.g., shade tolerance, soil nutrient cycling, phenotypical plasticity, animal and plant parasites and pollinating insects, etc.) creating traits for invasion. In particular, studies on A. platanoides unveiled such invasive advantages of this tree species as:

• high shade tolerance and adaptation;

• light interception that reduces light availability (both quantitatively and qualitatively) for native communities (e.g., Reinhart et al. [START_REF] Reinhart | Invasion through quantitative effects: intense shade drives native decline and invasive success[END_REF]); • physiological mechanisms including early leaf expansion and late leaf drop for a longer growth season compared to native species (Webb et al. [START_REF] Webb | The myth of the resilient forest: case study of the invasive Norway maple (Acer platanoides)[END_REF]); • allocation plasticity (Urgenson et al. [START_REF] Urgenson | Multiple competitive mechanisms underlie the effects of strong invader on early-to late-seral tree seedlings[END_REF]) changing patterns of dominance due to higher inherent growth rate, by increasing nutrient availability (i.e., Ca, Mg, K, N) and their recycling rates (Gómez-Aparicio et al. [START_REF] Gómez-Aparicio | Neighbourhood models of the effect of the invasive Acer platanoides on the tree seedling dynamics: linking impact on communities and ecosystems[END_REF]).

Ultimately, these specific properties of A. platanoides form important drivers of native suppression, leading to decreased survival and growth of native species (e.g., Reinhart et al. [START_REF] Reinhart | Invasion through quantitative effects: intense shade drives native decline and invasive success[END_REF]). The module "Factors" will take into account the history of past disturbances experienced by the ecosystem under consideration, both natural (e.g., fire, flooding, extreme weather events, diseases, herbivory, etc.) and anthropogenic (e.g., pollution, habitat destruction, introduced pets and pathogens, logging, climate change, dam and road construction, etc.). As the outcome, the module implements an assessment of individual and compound effects of the contributing factors from the perspective of potential drivers creating favourable conditions for successful invasion of nonnative species (e.g., Reinhart et al. [START_REF] Reinhart | Invasion through quantitative effects: intense shade drives native decline and invasive success[END_REF]).

The entry phase of invasion is characterized by the native tree canopies and exotic invaders attempting to establish themselves in the understory. The dynamics of the process may vary with a particular tree development stage. For example, the establishment of seedlings is viewed as a key phase in forest succession (Martin and Canham [20]; Morrison and Mauck [START_REF] Morrison | Experimental field comparison of native and non-native maple seedlings: Natural enemies, ecophysiology, growth and survival[END_REF]) during which closely related species are more likely to compete against each other on the basis of their shared characteristics (Lapointe and Brisson [START_REF] Lapointe | A comparison of invasive Acer platanoides and native A. saccharum first-year seedlings: growth, biomass distribution and the influence of ecological factors in a forest understory[END_REF]). Comparing very similar native and exotic congeners (e.g., A. sacharum and A. platanoides) in situ, it is recommended to identify which traits promote invasiveness (Mack [18]). In particular, while observing that Norway maple is highly shade-tolerant, it was reported that this species is more shade-tolerant as a seedling than as a maturing tree (Nowak and Rowntree [START_REF] Nowak Dj Rowntree | History and range of Norway maple[END_REF]; Wyckoff & Webb [START_REF] Wyckoff | Understory influence of the invasive Norway maple (Acer platanoides)[END_REF]), thus, conferring it invasive advantages by enabling to dominate in the understory and displace or diminish the native A. sacharum during the seedling stage.

3.2

Module "Ecosystem Modelling"

The "Ecosystem Modelling" module provides a formalized description of the invaded ecosystem. The abiotic (or non-living) pool includes physical factors (e.g., temperature, light, pressure, energy, acidity measure, soil depth, soil moisture-retention capacity, etc.) and chemical factors (e.g., oxygen, carbon, phosphorus, nitrogen, sulphur, calcium, etc. levels and availability). The biotic (or living) pool is organized in hierarchical structures of organisms depending on their roles in the energetic and metabolic processes at the overstory, understory and soil levels. Invaders will compete with native species for resources (e.g., light, space, mineral nutrients, etc.), and new traits in the ecosystem can be formed as a result (e.g., novel symbiotic mutualisms, means of acquiring resources, adaptation plasticity, allelopathic compounds, amplifying of native traits, etc.). The invasion of alien species will alter the composition, structure and functioning of the invaded ecosystem as well as the services it generated before the invasion occurred. The ultimate task of this module is to predict any such transformations. Some of the computational formulae are shown in ( 1)-( 6). Seed germination is estimated as:

𝑁𝑁𝑁𝑁𝑁𝑁 = %𝑁𝑁 * 𝑁𝑁𝑁𝑁𝑁𝑁, (1)
where NSP, NSG are number of seeds planted and germinated, respectively; %G is germination rate, as defined by Ologundudu et al. [START_REF] Ologundudu | Effect of light stress on germination and growth parameters of Corchorus olitorius, Celosia argentea, Amaranthus cruentus, Abelmoschus esculentus and Delonix regia[END_REF]. The cumulative growth of biological organisms at the juvenile phase occurs with the accelerating rate, at the mature phase -with the constant rate and at the senescent phase -with the decelerating rate.

The lifespan cumulative growth curve (CGC) is, thus, sigmoidal, and a growth variable (e.g., DBH, height, volume, etc.) can be found from the following formula (Brack and Wood [START_REF] Brack | Forest mensuration -measuring trees, stands and forests for effective forest management[END_REF]):

𝐷𝐷𝐵𝐵𝐻𝐻 = exp �𝑎𝑎 + 𝑏𝑏 𝑇𝑇𝑇𝑇 �, ( 2
)
where TA is the tree age; a and b are species-specific regression parameters. Non-linear (sigmoidal) growth functions are widely used in developing tree height-diameter relationships (Huang et al. [START_REF] Huang | Comparison of nonlinear height-diameter functions for major Alberta tree species[END_REF]). Zhang [START_REF] Zhang | Cross-validation of non-linear growth functions for modelling tree heightdiameter relationships[END_REF] investigated the performance of six functions and concluded that each growth function was equally well-fitted to data of ten species collected in the inland Northwest of the United States with a minor superiority of three functions, one of which is the Weibull-type formula (Yang et al. [START_REF] Yang | The potential of Weibull-type functions as a flexible growth curves[END_REF]):

𝑇𝑇𝐻𝐻 = 1.37 + 𝑐𝑐 * [1 -exp(-𝑑𝑑 * 𝐷𝐷𝐵𝐵𝐻𝐻 𝑓𝑓 )], (3)
where TH is the total tree height; c, d and f are species-specific parameters. Crown width (CW) can also be estimated as a function of DBH (Peper et al. [START_REF] Peper | Equations for predicting diameter, height, crown width, and leaf area of San Joaquin Valley street trees[END_REF]):

𝐶𝐶𝐶𝐶 = exp � 𝑀𝑀𝑀𝑀𝑀𝑀 2 * [𝑔𝑔 + ℎ * log(log(𝐷𝐷𝐵𝐵𝐻𝐻 + 1))]�, ( 4
)
where MSE is the mean standard error; g and h are species-specific parameters. Light regime in forest is largely determined by the overstory canopy gaps (CG), which can be predicted from:

𝐶𝐶𝑁𝑁 = 𝐷𝐷 𝑖𝑖,𝑗𝑗 -(𝐶𝐶𝐶𝐶 𝑖𝑖 + 𝐶𝐶𝐶𝐶 𝑗𝑗 )/2, (5)
where Di,j is the distance between two neighbouring trees; CWi and CWj are their respective crown widths. Then, the light level (%L) will be found as:

%𝐿𝐿 = 𝐶𝐶𝐶𝐶 𝐷𝐷 𝑖𝑖,𝑗𝑗 * 100%. (6)
In the open, %L = 100% (i.e., full sun regime) while for the closed canopies, %L = 0%. Formulas ( 5) and ( 6) are a simplified version of the gap light index by Canham [START_REF] Canham | An index for understory light levels in and around canopy gaps[END_REF]. b,c,d,f,g,h Species-specific parameters Dependant variables (Table 1) in formulas ( 1)-( 6) are computed under the assumption of optimal (i.e., most favourable) conditions for the dynamics of contributing processes. In reality, they will be affected by environmental, biological and anthropogenic influences which can be factored through the corresponding transformation functions for each affected state variable and for each type of the influence (Khaiter [START_REF] Khaiter | Environmental assessment of anthropogenic impact through the patterns of ecosystem stress reactions[END_REF]), i.e.:

𝑥𝑥 𝑖𝑖 𝑇𝑇 = 𝑇𝑇𝑇𝑇 𝑖𝑖,𝑘𝑘 * 𝑥𝑥 𝑖𝑖 𝑈𝑈 , 0 ≤ 𝑇𝑇𝑇𝑇 𝑖𝑖,𝑘𝑘 ≤ 1, ∀𝑖𝑖 = 1, . . , 𝑛𝑛, ∀𝑘𝑘 = 1, . . 𝐾𝐾, (7)
where 𝑥𝑥 𝑖𝑖 𝑈𝑈 and 𝑥𝑥 𝑖𝑖 𝑇𝑇 are the ith state variable before and after the influence of the kth type factor, respectively; 𝑇𝑇𝑇𝑇 𝑖𝑖,𝑘𝑘 is the transformation function of the kth type factor on ith state variable. A compound effect of multiple factors can be expressed through the resulting transformation function (TFR) built either from the ecological Liebig's law of the minimum of limiting factors [START_REF] Gutiérrez | Toward an integrated ecosystem perspective of invasive species impact[END_REF] or in the multiplicative form [START_REF] Herron | Invasive plants and their ecological strategies: prediction and explanation of woody plant invasion in New England[END_REF]:

𝑇𝑇𝑇𝑇𝑇𝑇 = min 𝑘𝑘=1,..,𝑟𝑟 {𝑇𝑇𝑇𝑇 𝑘𝑘 }, (8)
𝑇𝑇𝑇𝑇𝑇𝑇 = ∏ {𝑇𝑇𝑇𝑇 𝑘𝑘 }. 𝑟𝑟 𝑘𝑘=1 (9)

Module "Management"

The "Management" module generates possible scenarios of management interventions to cope with the invasiveness. It should be noted that measures can be taken at different decision-making levels, which can be divided into three categories: (1) decisions about preventing the entry of potentially invasive species (through existing laws, agreements on ballast water and shipping, etc.); (2) decisions about targeted control of invasive species after they have been introduced, whether purposely or accidentally; and (3) restoration of invaded habitats and ecosystems. For example, to restore an old-growth oak forest at the Washington Grove in Rochester, NY to its original and native state, Rogers [START_REF] Rogers | Invasion ecology of Acer platanoides in an old-growth urban forest[END_REF] recommended a two-step approach. The first step foresees the removal of mature A. platanoides trees from the forest area, along with other invasive species, which would also create open canopy conditions. By also removing A. platanoides trees from the streets near the Grove and replacing them with stress-resistant native trees, the propagule pressure of invaders will be reduced. The second step of the management plan would encourage native species diversity and oak recruitment by installing physical barriers inhibiting disruption of the leaf layer.

Once the set of scenarios is formed, the module executes predictions of ecosystem components, their short-and long-term dynamics, ecosystem persistence capacity and restoration capabilities in response to each potential managerial effort. It takes into account the mechanisms of invasion, typology of stresses and the common patterns in the ecosystem stress behaviour. As it was mentioned above, a high level of uncertainty concerns each phase of the invasion and also when controls are being applied (Maguire [19]). Thus, woody plant invasion of shade-tolerant species can endure periods of suppressed growth before reaching the canopy layer, which, in combination with the long generation time of trees, makes it difficult to perceive as it unfolds, and, hence, difficult to manage in its early stages (Herron et al. [START_REF] Herron | Invasive plants and their ecological strategies: prediction and explanation of woody plant invasion in New England[END_REF]; Lapointe and Brisson [START_REF] Lapointe | A comparison of invasive Acer platanoides and native A. saccharum first-year seedlings: growth, biomass distribution and the influence of ecological factors in a forest understory[END_REF]; Martin et al [START_REF] Martin | Why forests appear resistant to exotic plant invasion: intentional introductions, stand dynamics, and the role of shade tolerance[END_REF]; Wangen and Webster [START_REF] Wangen | Potential for multiple lag phases during biotic invasions: reconstructing an invasion of the exotic tree Acer platanoides[END_REF]; Webster et al. [START_REF] Webster | Woody invaders and the challenges they pose to forest ecosystems in the eastern United States[END_REF]).

The resilience limits of invaded ecosystems also remain unclear. Theoretical ecologists question the ability of a forest ecosystem to fully recover to the original, preinvaded state in the face of complex interactions among anthropogenic impacts: forest fragmentation, climate change and the introduction of invasive species (Webb et al. [START_REF] Webb | The myth of the resilient forest: case study of the invasive Norway maple (Acer platanoides)[END_REF]).

Given various sources of uncertainty and likely significant cost associated with the implementation of controls in view of scarce budgeting resources, a risk analysis becomes a necessary step of the decision-making process. Specific features of risk analysis in application to the cases of biological invasion have been examined by Andersen et al. [START_REF] Andersen | Risk analysis for invasive species: general framework and research needs[END_REF] and Bartell and Nair [START_REF] Bartell | Establishment risk for invasive species[END_REF]. The outcome of this module and the entire tool will be a set of recommended measures aimed at addressing the intervention of alien species in the most efficient way and suggesting resilient solutions for the impacted ecosystems.

Discussion and conclusions

In this paper, we presented functional architecture of the software tool and its internal structural logic, focusing on the modelling module as a backbone of the framework. The principles of multi-layered designs and platform independence have been applied at this stage. The latter enable future implementations of the framework suitable for various targeting environments. At the same time, the transition from the current stage of architectural design to detailed design and further to implementation phase of the software tool described above will require the following groups of models:

• models of pre-invaded dynamics of native species and the ecosystem as a whole; • models of invasive dynamics of the alien species, including mechanisms underlying their invasive success; • models of invasive-resident species interactions (e.g., competition for light, space, nutrient resources; possible symbiotic mutualisms, etc.) and their modification by biotic and environmental drivers; • models of persistence capabilities to sustain the invasion and probable transformations in invaded ecosystems; • models of effect of invasion on critical functions of the ecosystem; that is ecosystem services; • models predicting ecosystem components, their short-and long-term dynamics, ecosystem persistence capacity and restoration capabilities in response to each potential managerial effort or scenario; • models aimed at selection of the best possible scenario for managing the invasion

Prior to the realization of the tool, a number of non-trivial issues should also be addressed. This includes, e.g., predictions of the invasive stress dynamics of the ecosystems. It appears that prediction of the invasive potential of a certain alien species to invade a given environment can be viewed as a problem of machine learning and solved by classification algorithms, provided that sufficient volumes of relevant empirical data are accumulated and available. Prediction of endogenous ecosystem dynamics caused by biological invasion and resulting in compositional, structural and functional transformations most likely calls for process-based models.

There is a view that an integrated ecosystem perspective of invasive species is amenable to mathematical formalization and system dynamic modelling (Gutiérrez et al. [START_REF] Gutiérrez | Toward an integrated ecosystem perspective of invasive species impact[END_REF]), and it is shared by the authors. It also is a subject of our ongoing endeavours on the topic of decision-making and management of biological invasion.

Fig. 1 .

 1 Fig. 1. Phases of invasion process (after Radosevich et al. [30]; Andersen et al.[1])

Fig. 2 .

 2 Fig. 2. Tree development stages

Fig. 3 .

 3 Fig. 3. The EMDMIC functional architecture

Table 1 .

 1 Characteristics of the variables and parameters in (1)-(6).

	Variable symbol Description	Unit
	NSP, NSG	Number of seeds planted and germinated
	%G	Germination rate	%
	TA	Tree age	years
	DBH	Diameter at breast height	m
	TH	Total tree height	m
	CW	Crown width	m
	D	Distance between two neighbouring trees	m
	CG	Canopy gap	m
	%L	Light level	%
	a,

Acknowledgements. The authors would like to express appreciation to all researchers whose publications are referred to in this paper for their field studies and theoretical generalizations on invasive species which inspired our interest towards the topic. The authors are thankful to the editor and anonymous reviewers for their helpful suggestions and comments on the early versions of the manuscript which helped to improve the quality.