José Tarcísio Franco De Camargo

Estéfano Vizconde Veraszto
email: estefanovv@cca.ufscar.br

Adriano Aparecido Lopes
email: adrianoalopes2008@yahoo.com.br

Tainá Ângela

Vedovello Bimbati
email: tavbimbati@gmail.com

Information Technology and Solid Residue Management: a Case of Study Using Freeware and Social Networks

Keywords: Waste disposal, recycling, computational tools, sustainability

Separation, collection, processing and disposal of waste considered recyclable are currently one of the greatest challenges of human beings in their quest for sustainability. With consumption levels in ever-higher levels, the reutilization of recyclable waste, which would likely target the garbage, becomes an obligation of society as a whole. Thus, addressing in an appropriate manner the issue of selective collection of recyclable materials can contribute to the solution of several problems associated with it -mostly under the views of environmental, social and economic ways. This way, the Project iCARE (Instrumentation for the Assisted Collection of Recyclable Waste) is based on an experiment that aims to provide computational tools that could significantly contribute to the issue of the reuse of solid recyclable waste. Specifically, the iCARE software aims to contribute to the integration between donors and collectors of recyclable materials, establishing a communication channel where ordinary people or companies may announce the availability of recyclables to collectors. These, in turn, can check through the same software the availability of certain wastes, coming to contact consumers for possible collection and subsequently forward the waste to processing companies. This software also offers a "routing module", which allows the collector to establish an optimized collection route according to certain criteria. All software developed in iCARE's framework must provide a user friendly interface, making its use very simple. iCARE is a project of free use and also a social tool, which seeks to contribute to the solution of the complex problem of disposal of recyclable waste.

Introduction

Nowadays, there is a consensus that the sustainability of life on the planet necessarily passes by the rational use of the limited resources available. The human being realized that the unbridled consumption inevitably will lead to a depletion of the natural sources. Thus, separation, collection, processing and disposal of waste considered recyclable are, currently, one of the great challenges of humanity in the quest of sustainability. With high levels of consumption, the reutilization of recyclable waste, which would likely target the landfills, has become an obligation of society as a whole [START_REF]United Nations of Environment Program. Global Environment Outlook 3[END_REF][START_REF] Foray | Technology and the environment: an overview[END_REF][START_REF] Freeman | The greening of technology and models of innovation[END_REF].

In this way, the planning and implementation of programs dedicated to the selective collection of recyclable materials can contribute to the solution of various environmental, social and economic problems. Under the economic point of view, the selective collection effectively promotes the solution of some problems relevant to society, considering that discarded materials can go back to the cycle of consumption, thus reducing the use of natural resources in the manufacture of new products.

Under the environmental point of view, the reuse and recycling of materials would probably promote direct impact on the preservation of natural resources, conserving raw materials, water and energy in production processes and, consequently, a decrease in the amount of trash sent to landfills as well as its useful life [START_REF] Foray | Technology and the environment: an overview[END_REF]. In the social aspect, the selective collection has positive impact on socioeconomic conditions of recyclable material collectors, who are the first actors in the selective collection and recycling chain.

In this context, this work presents an experiment carried out in a city in Sao Paulo State, Brazil. The work that is being carried out includes the creation of computational tools which intend to support the process of selective collection of recyclable materials. At first, a software called "iCARE bulletin board" was developed, which aims to promote the integration between "consumers" (recyclable waste producers) and "pickers" (responsible for the collection and disposal of waste). The following section presents the fundamentals of this project within the region where it is developed.

Science, Technology and the Environmental Issue

Having the Club of Rome as an important delegate, environmentalists from the decades of 1960 and 1970 gave emphasis that world growth was limited due to accelerated population increase, to deficiency of agricultural production (aggravating to hunger), the exhaustion of natural resources, the destruction of the environment and the increase in global industrial production. This vision, transparent in the work of Meadows [START_REF] Meadows | The limits to growth[END_REF][START_REF] Meadows | Beyond the limits[END_REF] had a remarkable accession in the postwar period, with questions about the side effects of technological development on society and economy. The dangers brought by pollution and by widespread environmental degradation were key factors for a resumption of the Malthusian paradigm, whose central idea stressed that the saturation of the world growth would be in a hundred years [START_REF] Barnett | Scarcity and Growth: the economics of natural resources availability[END_REF][START_REF] Corazza | Políticas públicas para tecnologias mais limpas: uma analise das contribuições da economia do meio ambiente. Tese de doutorado. Instituto de Geociências[END_REF]. From the years 1970, began a series of investments in clean technologies or alternatives in various countries in response to the demands of the environmental movement. In the decades of 1980 and 1990 the debate on environmental issues was expanded and relations with the technology got new directions. The Bariloche Group, along with other world references, was pointing out that the economic system has self-regulatory mechanisms that allow modification or reversal of patterns before the system reaches the point of catastrophe [START_REF] Freeman | The greening of technology and models of innovation[END_REF][START_REF] Andrade | Inovação tecnológica e meio ambiente: a construção de novos enfoques[END_REF]. In the decade of 1980, sustainable development was defined and studies aimed at reducing poverty and alleviating social problems were initiated [START_REF]United Nations of Environment Program. Global Environment Outlook 3[END_REF]. However, it was only in early 1990 that the concepts of previous years have been implemented. The concept of the role of technology on environmental issues has changed, enabling the belief that it is possible to recover degraded areas and increase the efficiency of natural resources in parallel with the increase of productivity. The technology thus is seen not as an external factor, but as an integral element of the decision-making processes. The development of S&T adapted to environmental issues then must know how to handle these challenges and dilemmas by internalizing the environmental variable in the process of innovation and seeking efficiency and quality in the development of new products, processes and/or services. (Passing not only by raising awareness about environmental issues, but also considering legal and economic aspects) [START_REF] Foray | Technology and the environment: an overview[END_REF][START_REF] Benedick | Tomorrow's is global[END_REF][START_REF] Bin | Inovação e meio ambiente na pesquisa agrícola[END_REF]. In view of the foregoing, we can establish relationships with other works [START_REF] Herrera | Las Nuevas Tecnologías y el Futuro de América Latina[END_REF][START_REF]Em português: Comissão Mundial sobre meio ambiente e desenvolvimento. Nosso futuro comum[END_REF] to point out that both the technology and social organization can be managed and improved in order to provide a new era of economic growth, so that humanity will be able to make sustainable development possible. Thus, environmental, scientific and technological developments are not separate challenges. Making the environmental issue the record on political agendas is an item of great importance with regard to the economy and resources [START_REF] Herrera | Las Nuevas Tecnologías y el Futuro de América Latina[END_REF][START_REF]Em português: Comissão Mundial sobre meio ambiente e desenvolvimento. Nosso futuro comum[END_REF][START_REF] Healy | Science, technology and future of sustainability[END_REF].

The development of the S&T front of environmental issues can become more efficient if supported by a strong and articulate public policy. It is also necessary to create and maintain mechanisms to link research and development in the sectors of production to their demands, as well as the creation of new energy sources and new materials, in order to seek solutions to social problems [START_REF] Corazza | Políticas públicas para tecnologias mais limpas: uma analise das contribuições da economia do meio ambiente. Tese de doutorado. Instituto de Geociências[END_REF][START_REF] Benedick | Tomorrow's is global[END_REF][START_REF] Grübler | A Model of EndogenousTechnological Change Through Uncertain Returns on Innovation[END_REF].

It is in this sense that the work proposed here is developed. Thus, the aim of this paper is to contribute to sustainability through methods and techniques that promote technological innovation and social inclusion. These are the points that will be presented in the sequence.

A Local Context to the Issue of Waste Recycling

The steps in the process of collection and disposal of recyclable waste can be viewed in a simplified way, as follows:

1. Separation: this is the step where each citizen must separate, from non-reusable waste, those materials which can be recycled. This is a step based on education and awareness of people about the need to reuse materials that can go back to the cycle of consumption. 2. Collection and Disposal: this step has a "logistic profile", since it involves several costs for those that are collecting reusable materials. Collect which materials? Which is the minimum amount viable for collecting? How to collect and give destination? These are some of the main questions to be considered at this stage. 3. Processing: as well as the collection and disposal stages, this one can also be seen as a "business", where a particular company may have focused on the reuse of recyclable waste and its subsequent commercialization. This is the step of the recycle chain that is more distant from "ordinary people", considering that here the "main actor" is a businessman.

In this chain, the "picker" (or "collector") is the leading actor in the production of recycled materials, making approximately 80% of all the work, collecting an average of 600 kg of materials per day [START_REF] Cbo | Movimento Nacional dos Catadores De Materiais Recicláveis[END_REF].

The project dealt with in this paper was born in the city of Mogi Guaçu, Sao Paulo State, Brazil. It is a medium-sized town, with an expanding industrial pole and a large number of companies in the service sector. This city has possessed, approximately fifty years ago, an economy almost entirely based on agriculture, and experienced an intense population expansion on the last forty years, when the population jumped from about 30,000 to 130,000 inhabitants. This expansion was due mainly to the migration of people from other states of the country, searching for new jobs, which actually ended up experiencing social exclusion. As a consequence, the population of recyclable materials collectors came to be composed mostly by migrants who come from other regions to work in the rural area and ended up facing unemployment. Excluded from school for avoidance or lack of access, they are also excluded from the labour market and therefore of society. To escape from hunger, these people found the possibility to work in the city from collecting discarded materials.

Regarding the problem of the garbage generated and processed in the city, according to data presented by the Secretary of Municipal Services, the rate of garbage collection in the city is around 99%. Among the total municipal waste generation, the share of production of household garbage by the population is approximately 105 tons/day, of which 32% are recyclable materials, 52% are organic and 16% are considered waste.

In this scenario, a matter that can be displayed is: how to integrate consumers, who generate such amount of potentially recyclable material (that is currently going to junk), and collectors, who while may give the appropriate disposal waste can also improve their source of income? It is in this scope that iCARE presents itself, as a technological arm that aims to generate computational tools that will contribute to the solution of socio-economic and environmental issues, among them the problem of proper collection and disposal of recyclable materials.

Computational Tools to Support Selective Collection

Although described in a simplified way in this paper, the complexity of the presented chain induces specialization from "actors" involved in the same. The proposal here introduced intends to contribute significantly to the integration of the actors responsible for the second step: consumers who generate recyclable waste and pickers who dispose of these materials. Currently, with the expansion of the internet connection services, a considerable part of the population has access to the World Wide Web in their own homes, and among the most common habits of this population, is the communication through social networks. These new mechanisms of communication have promoted the exchange of messages and information in a manner so intense that its potential currently transcends social barriers. Even the lowest economic classes of the population have access to cheap portable devices which allow access to all these networks.

In this scenario, the software created under this project aims to contribute to the integration of "producers" and "pickers", establishing a communication channel where ordinary people or companies may announce the availability of recyclables to possible collectors. These, in turn, can check via software the availability of certain wastes, contact consumers for collection and subsequently forward the waste to processing companies.

In order to facilitate the collection and disposal of waste collected, this software also offers a "routing module", which allows the collector to establish an optimized collection of routes, according to certain criteria (distance and time of travel, for instance).

This software was built to provide a "friendly" interface, making its use very simple. In its current stage, the only preconditions for this software to be used are a "JAVA ready" device and an internet connection for access to data.

This software aims as well to keep a democratic and decentralized spirit. Democratic in the sense that its use and sharing is free for anyone, either to announce or to collect recyclable or reusable materials; and decentralized because any community can implement its own network, answering specifically the subject of a particular region.

Introducing iCare

As mentioned previously, iCARE means "Instrumentation for the Assisted Collection of Recyclable Waste". This software provides a tool for the communication between donors and collectors of recyclable materials, in this case, a Java application that is a virtual "bulletin board", through which users can make and schedule waste collections, and a database, which will store data about recyclable materials offered for disposal. Any community may set up its own database in a local server, providing data to the local citizens or companies.

To provide data, a local server must run "Apache Derby", an open source RDBMS that is based on Java technology and SQL, which will manage the database where all information will be stored. This database will serve users through a "bulletin board", written in Java, which will promote the interaction among users.

Currently, this bulletin board was built in order to provide the following functionality for its users:

• an "interface for sending messages", through which an user can offer the donation of materials that can be recycled; • an "interface for receiving messages", through which an user can check the materials available for collection and contact the respective donors to this schedule; • a "route generator interface", through which a collector can establish a collection of routes through certain criteria.

The interface for sending messages is presented in Figure 1. Through this feature, the user can send to the Bulletin Board a list of recyclable materials that would be available for collection. The user can select from a list the materials that he wants to make available, indicating the available quantity. The user can insert more than one type of material for target.

The interface for receiving messages is shown in Figure 2. The feature "Receive Message", allows collectors to receive these messages on their devices and, in this way, select the materials that interest them and contact the respective disposers to schedule a pickup.

The last feature available is the interface for creating collection routes, where is possible to schedule the gathering of recyclable materials. The problem then becomes the definition of the best way to carry out the collection and disposal of materials. The interface for creating routes allows the user to enter multiple addresses for collection and an optimized route will be calculated. This feature is based on Google Maps Javascript API. Figure 3 presents a picture of the interface for calculating routes.

Concluding Remarks

The iCARE project aims to contribute with the separation, collection and proper disposal of solid wastes, as a way to minimize the socio-economic and environmental problems arising from the inappropriate treatment of waste. The use of this software has allowed the operation of a structure for the collection and disposal of waste, which has been organized by the Association of Collectors of Mogi Guaçu, SP, Brazil, ensuring the entry of materials to the collective work of everybody involved. The proposal of a programmed logistics makes selective collection activity itself less impressive due to the reduction of fuel consumption and emission of greenhouse gases in the collecting of materials with the planning of routes.

Considering the Brazilian national solid waste policy [16], in compliance with the law Number 12,305/2010, regarding the separate collection, recycling, reverse logistics and the strengthening of the concept of shared responsibility, the software comes to interact with companies and organizations in the area of social and environmental responsibility for the correct disposal of waste generated by its activities, with the obligations of all links in the production chain. There is also a reformulation of the concept of waste, because it happens to be seen by society as a material of value, and that the generator is responsible for their proper disposal.

The iCARE project is still in an early-stage and seeks to involve the largest possible number of people interested in the collection and disposal of recyclable waste. Other initiatives are planned for future work within the scope of the project. Among them we can mention the mapping of disposal points, which could be coupled directly to the module "Bulletin Board".

Fig. 1 .

 1 Fig. 1. Interface for sending messages.

Fig. 2 .

 2 Fig. 2. The interface for receiving messages.

Fig. 3 .

 3 Fig. 3. Interface for route calculation.

adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011


Miroslav Kubásek
email: kubasek@iba.muni.cz

Civic Issues Reporting and Involvement of Volunteers as a Phenomenon in the Czech Republic

Keywords: Crowdsourcing, smartphones, illegal landfills, geolocation, web portal, GIS, mapping, human sensors

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

Introduction

Illegal waste dumping is a serious environmental concern in many countries [START_REF]Europol warns of increase in illegal waste dumping[END_REF][START_REF] Hccrems | Hunter & Central Coast Regional Environmental Management Strategy Illegal Dumping[END_REF]. These illegal dumps decrease the quality of human life in surrounding areas. Illegal dumping of garbage, discarded appliances, old barrels, used tyres, furniture, yard debris, oil, antifreeze and pesticides can threaten human health, wildlife and the environment.

Engaging citizens is challenging but due to the proliferation of personal smartphones with geolocation it is easier to develop a reporting system that can be used by every citizen. Timely elimination of illegal dump sites can limit the extent and severity of the damage (soil and water contamination, hazard of fire etc.).

Solving these issues was the main reason for creating the ZmapujTo. Project was started in mid 2011 as an e-Participation project and became the most popular environmental project in the Czech Republic. ZmapujTo is based on community phenomenon and thus leverages the power of crowd. It enables each one of us to be heard [START_REF] Kubásek | Crowdsource Approach for Mapping of Illegal Dumps in the Czech Republic[END_REF][START_REF] Kubásek | Mapping of Illegal Dumps in the Czech Republic -Using a Crowd-Sourcing Approach[END_REF][START_REF] Kubásek | Involving Citizens into Mapping of Illegal Landfills and other civic issues in the Czech Republic[END_REF].

The ZmapujTo Web Portal

In this chapter we shortly explain the framework of the Web portal ZmapujTo and introduce the technology of interactive mapping which enables detailed viewing of reported issues. We use several advanced GIS technologies to improve the visualization of reported illegal dumps (Fig. 1).

The first technology is the Google Street View 1 , which enables users to get a very detailed overview of the situation around the reported illegal dump. The Google Street View is a feature of Google Maps that provides 360° panoramic street-level views and allows users to view parts of selected cities and their surrounding metropolitan areas at ground level. The Google Street View displays photos that were previously taken by a camera mounted on an automobile. Due to the fact that illegal dumps are mostly located near roads, we found the Google Street View as a useful tool to add a situation overview about the reported illegal dump.

Fig. 1. Framework of the project ZmapujTo

Next we integrate the cadastral maps of the Czech Republic2 to the web portal ZmapujTo. This technology can simply identify the owner of the illegal dump site and project a specific plot of this. The municipal authority can thus directly contact this owner.

The map of illegal dump sites also provides a set of layers which can be explored by the users. The user can select from the menu of the portal the ortofoto layer, hybrid layer and a terrain layer.

We gained experience using the initial version of ZmapujTo portal since 2011 and we have collected a list of opinions, proposals and criticisms. For example a lot of municipalities wanted to extend the types of reports (we had only one possibility to report illegal dumps, so they proposed to extend reports e.g. about overloaded bins). Some authorities closely cooperated with their city police department to help monitoring illegal dumps in their cities. They were interested to integrate a reporting system directly into the police information and communication systems etc.

Fig. 2. Welcome page of ZmapujTo

Based on user feedback we launched a new version of the ZmapujTo portal in Spring of 2014 (Fig. 2). The main properties of this new version are: an extension of report types, an extensive list of mobile platforms which will allow running the ZmapujTo mobile application, and an improved web page to employ a responsive design paradigm and enable the use of tablets and phones to browse and manage reported issues.

3

System Architecture

The general system architecture (Fig. 3) is proposed to utilize existing technical solutions and services as much as possible. We have tried to design the system so that it can be deployed on a distributed environment if it will be necessary, e.g. in case of large traffic. px). Original images we uploaded into Amazon S315 online file storage to keep them for possible future use.

Fig. 3. System architecture

We created RESTful server with several APIs, where we combine the best of NodeJS and PHP worlds. We use NodeJS mostly for critical, the most widely used parts (e.g. filtering of reports in map, on-line providing of the data).

In our system we have proposed four APIs. The first "Public API" is intended to be a backend server for mobile applications. Its main objective is to receive photos and reports from mobile applications and from on-line form. The second API "Web API" is intended to serving data for web application (based on AngularJS framework). "Admin API" with authentication is intended to municipalities, which are able to manage reports in their regions. Last "Manage API" is used by supervisor and enable to manage and processing of new reports, processing of photos, management of email notifications etc.

For email notifications of municipalities we utilized the Mandrill16 which has a easy to use really good API. For processing of emails from municipalities we simply utilized Gmail17 service. We use email prefixing (append a plus "+" sign and any combination of words or numbers after your email address) to enable automatically unique identification of related report.

The system in this configuration works very well for last 10 months. In this time period we did not notice loss of services used. System responses are despite the growing interest in ZmapujTo still very fast and sufficient.

Efficient Report Management

The key issue of the ZmapujTo reporting system is the communication with municipal authorities. In the ZmapujTo portal, the responsible municipality will receive information about the reported issue by e-mail. Municipal authorities have two possibilities to solve this event:

1. Either to answer directly to this email with response. Mostly it is information on whether they are aware of this issue or not. These responses are automatically processed and the content of these response e-mails are inserted directly to the record list of specific events; 2. Or to log-in into ZmapujTo web portal and process the report directly in its administration interface (Fig. 4). The aim of this application is to provide a simple tool, which will give to the authorities actual information about reported issues in their district. The administrator of the municipality assigned to a specific issue can change its, change location or leave comments as well. The administrator can also use the statistics of the district and a set of widgets (Fig. 5), which can be placed into the municipality web site and thus inform the visitors that the municipality is involved in the project, and how many issues have been reported and solved

Voluntary Event

In 2014 for the first time, an event called "Ukliďme Česko" (Clean up the Czech Republic) took place in the Czech Republic (Fig. 6). It was inspired by past events from abroad (e.g. "Let's do it!"18 ), and hopes to address the continued lack of success in cleaning the dumps. The main goals were to warn and inform the public about the problems of illegal dumps and subsequently clean them up with the help of volunteers.

The cleaning was scheduled for 17th May 2014. This cleaning event collaborated closely with the ZmapujTo project and used its platform for mapping and registration of illegal dumps. The event was driven by volunteers, municipalities and nongovernmental organizations.

"Ukliďme Česko" participants were divided into groups, with having at least one organizer per group. The organizer was responsible for arranging group meetings in order to discuss various issues regarding the event.

One volunteer in the group used an interactive map (Fig. 7) on the web portal to register. Dark-colored points are drawn on the map to show all the registration points. Each of these points contains information about the number of registered participants (organizers and volunteers separately) in a particular location. Therefore one can check whether there is any organized group already present in his place of interest or whether it is necessary to establish a new one. Those interested persons can register to one particular point at any part of a city or a village.

Fig. 6. Web page of the cleaning event

Thanks to the registration in this information system, organizers gain access (password required) to their account created directly on the web portal. Each organizer can manage a number of volunteers, choose a meeting point and an cleaning area (illegal dumps from ZmapujTo project are seamlessly integrated, so the organizer can select specific illegal dumps, which going to be cleaned up). This organizer's account also mediates communication with associated volunteers and with other organizers, for example from their neighborhoods.

Conclusion

The ZmapujTo project fulfills its aim to inform the public of the Czech Republic about the dangers related to illegal dump sites, motivates the public to report them, and offers municipalities and other organizations a tool for administration of these reports.

We followed various feedbacks collected from users and developed a new version of the web portal with state-of-the-art information technologies. An extension of the report types and automatic communication with all municipal authorities in the Czech Republic are the main benefits of the new version. The user-friendliness has been improved by using up-to-date and more efficient technologies. We utilize existing technical solutions and services as much as possible. We chose young technology as a NoSQL MongoDB database, Node.js, Express web framework and AngularJS for creating Single Page Application. Despite the initial fears, we can tell now, that during the operation of the system we didn't encountered a single failure or a major problem.

During the event "Clean up the Czech Republic" which took place on Saturday 17th May 2014 the participants collected 350 tons of garbage, the volunteers participated in 280 places over the whole country. Based on feedback from the organizers and volunteers who used the ZmapujTo mobile application and the interactive map of volunteers we can state that the our systems fulfills our expectations.

Fig. 4 .Fig. 5 .

 45 Fig. 4. Reports management and interactive map

Fig. 7 .

 7 Fig. 7. Interactive map of volunteers integrated with illegal dumps from ZmapujTo

adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011

http://nahlizenidokn.cuzk.cz/

Amazon Simple Storage Service http://aws.amazon.com/s3/

https://mandrill.com/

https://mail.google.com

http://www.letsdoitworld.org/

We analyzed the state-of-the-art information and communication technologies (ICT) and decided to use a single page application approach of SPA 3 technology in order to improve the web portal response time. We selected the AngularJS 4 JavaScript 5 framework. We also applied the JavaScript language in cooperation with NodeJS 6 (JavaScript event-driven runtime) and Express 7 (the web application framework for nodeJS on the server side). We decided to leave traditional relation databases and used the document base NoSQL 8 database MongoDB 9 to store the data.

We selected the DigitalOcean 10 platform for hosting (it offers SSD cloud servers from $5 per month). Using this combination of ICT tools we have reached response times under 70 ms on user frontend events (e.g. map zoom, filter apply).

We also developed the new mobile application with Sencha Touch 11 HTML5 framework and ported it to Android, iOS and Windows Phone devices through Apache Cordova 12 .

All work data of ZmapujTo are stored into the MongoDB database. This database contains collections of reports, users and also additional geographical data (ArcČR 500 version 3.1 13 ). We widely used geospatial queries and we can say that in Mon-goDB they works without any problems. Data are backed up regularly (five times per day) on geographically remote location.

We do not store the images ourselves. Instead we use the excellent Cloudinary 14 service for fast CDN-based image delivery. Due to limited storage capacity and bandwidth of Cloudinary we store here only processed images (resized to 500x500


Sascha Schlobinski
email: sascha.schlobinski@cismet.de

Giulio Zuccaro
email: zuccaro@unina.it

Martin Scholl
email: martin.scholl@cismet.de

Daniel Meiers
email: daniel.meiers@cismet.de

Ralf Denzer
email: ralf.denzer@enviromatics.org

Sergio Guarino
email: guarino@unina.it

Wolf Engelbach
email: wolf.engelbach@iao.fraunhofer.de

Kuldar Taveter
email: kuldar.taveter@ttu.ee

Steven Frysinger
email: ste-ven.frysinger@cismet.de

Decision Making and Strategic Planning for Disaster Preparedness with a Multi-Criteria-Analysis Decision Support System

Keywords: impact scenario simulation, indicator, criteria, decision support system, multi-criteria-analysis, disaster preparedness, strategic planning

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

Introduction

Decision making and strategic planning for disaster preparedness can be extensively supported by simulations of crisis scenarios. Such scenarios can provide insight into the overall course, cause and effect of many aspects of a future crisis. A powerful high level way to look at a crisis is by relating hazards, the exposure of elements at risk and their vulnerabilities, resulting in probabilities of damage or other effects. A simulation following this concept will produce the so-called impact scenarios that show the potential damage of a crisis, e.g. the effect of an earthquake on buildings and population [START_REF] Zuccaro | Seismic Casualty Evaluation: The Italian Model, an Application to the L'Aquila 2009 Event[END_REF]. Impact scenarios are particularly suited to supporting strategic decision making as they focus on the causal connection between a specific threat and the potential effects on the objects of concern. The construction of impact scenarios requires the integration of a potentially large number of complex data sets either based on static data (such as census data) or simulation results (such as risk maps). These data usually come from various sources and thus are inherently heterogeneous with respect to format, resolution and semantics. One way to considerably reduce the complexity of impact scenarios while preserving their key properties is to aggregate scenario data into so called (key) performance indicators [START_REF] Engelbach | Indicators to compare simulated crisis management strategies[END_REF]. This kind of approach to complex data has a long history in economics and business management e.g. [3] and such indicators are the de-facto standard in measuring the performance of emergency services (e.g. [START_REF]Key Performance Indicators in EMS Zygowicz[END_REF]). While performance indicators pertaining to impact scenarios allow comparison of individual indicator values decision makers (DM) still face multiple, often-conflicting decision objectives involving more than one criterion. As a result, the selection of a specific scenario (e.g. intervention) with the "best" performance is very difficult. For example, a particular mitigation action will come with a cost but will reduce the potential impact of a disaster. So the objective of minimal cost for mitigation measures in combination with the objective of minimizing disaster impact leads to a trade-off decision problem (np-hard [START_REF] Garey | Computers and Intractability[END_REF]) where optimization approaches (for example) are hardly applicable in real world solutions. However, established methods of Multi-Criteria Decision Analysis (MCDA) e.g. [START_REF] Yager | On ordered weighted averaging aggregation operators in multi-criteria decision making[END_REF] offer solutions to the problem.

This paper describes a seamless decision support concept developed within the CRISMA FP7 project [START_REF] Heikkilä | Minimising the outcome of disasters by simulating the effects of different actions[END_REF] to connect simulated crisis scenarios and aggregated performance indicators of impact scenarios with state of the art Multi-Criteria Decision Analysis (MCDA) methods [START_REF] Steuer | Multiple Criteria Optimization: Theory, Computation and Application[END_REF].

Concept Overview

The overall idea is to: (a) Let the Decision Maker (DM) produce and use scenarios in support of the decision; (b) provide aggregated but representative information about scenarios (indicators); (c) support the DM in defining an explicit decision strategy (criteria, priorities, Andness and Orness (see section 3)); and (d) assist in comparing and ranking impact scenarios according to the decision strategy.

Fig. 1. Decision Support Concept Overview

The overall concept consists of seven elements -four data and three functional -to support the DM: (1) an impact scenario consisting of information required to take a decision, e.g. representing the possible consequences of a flood for people living in the flooded area; The DM can use the four data elements as a basis for the decision and define an individual decision strategy mapping indicators to criteria with the help of criteria functions. In addition they are supported in assigning priorities to indicators as well as defining the level of "Andness" and "Orness" of the ranking function [START_REF] Dujmović | Properties of Local Andness/Orness[END_REF] through the parameterization of a MCDA method [START_REF] Yager | On ordered weighted averaging aggregation operators in multi-criteria decision making[END_REF]. More concretely the DM is supported in:

• Using indicators derived from impact scenario data (usually aggregated) to quickly assess and compare impact scenarios • Defining a decision strategy by: ─ Mapping performance indicators to decision criteria (defining the level of satisfaction for each indicator) ─ Defining priorities by assigning weights to indicators ─ Defining the level of Andness and Orness to be considered when computing the rank of an impact scenario • Dealing with a multi-criteria decision problem by obtaining a ranking of scenarios with respect to the defined decision strategy.

Ordered Weighted Averages as a Means for Decision Support

Decision problems considering more than one criterion on the basis of impact scenarios require appropriate methods to assess the performance of specific scenarios. In our concept we have selected the Ordered Weighted Averages method (OWA) [START_REF] Yager | On ordered weighted averaging aggregation operators in multi-criteria decision making[END_REF][START_REF] Yager | Quantifier guided aggregation using OWA operators[END_REF][START_REF] Zuccaro | Multi-criteria analysis in vulnerability assessment[END_REF] that allows one to specify a particular decision strategy that defines the properties of a good solution. The OWA method allows us to:

• Implement several decision makers' perspectives (multiple points of view);

• Make the decision strategy explicit;

• Obtain a score/rank for each scenario;

• Let the DM choose between different strategies (e.g. optimistic, neutral, pessimistic); • Compare results obtained under different strategies.

The OWA method is based on multi-criteria aggregation operators proposed by Yager [START_REF] Yager | On ordered weighted averaging aggregation operators in multi-criteria decision making[END_REF]. OWA is characterized by a vector of ordered weights in addition to the importance weights assigned to each criterion. Using OWA, normalized indicator values are multiplied with a corresponding level of importance. The vector of weighted levels of satisfaction for all indicators is re-ordered according to their values and weighted according to their position in the vector. The vector of ordered weights determines an instance of an OWA operator. E.g. the vector of ordered weights (1, 0, …, 0) will give full weight to the criterion with the highest level of satisfaction independent of all other criteria (maximum level of Orness). As a consequence, alternatives with a single outstanding property will be ranked highest. This is called a risk-taking or optimistic decision strategy. In contrast, the vector of ordered weights (0, …, 0, 1) will give full weight to the criterion with the lowest level of satisfaction. As a consequence alternatives with the best "poor" criterion will rank highest (maximum level of Andness). This is called a pessimistic decision strategy. Obviously, between these two extremes there is a large number of intermediate strategies. Another easily interpreted strategy is the neutral strategy that does not emphasize any position in the re-ordered criterion values (simple weighted average). The vector of ordered weights can be calculated to fit to a specific decision strategy [START_REF] Yager | Quantifier guided aggregation using OWA operators[END_REF] but of course they can be defined manually by the DM as will be shown in the next section.

4

The Implementation

The concept described above has been implemented including a HTML5 [12] (Angular JS [13]) based User Interface (UI). The software is part of the CRISMA Integrated Crisis Management System Framework [START_REF] Dihé | An architecture for integrated crisis management simu lation[END_REF]. It is available under an open source license as a github project [15]. For illustration, the figures visible in the screenshots refer to three fictional alternative mitigation strategies for an earthquake in L'Aquila (Italy), since that is one of case studies addressed in the CRISMA project 1 .

Scenario Analysis and Comparison View

The Scenario Analysis and Comparison View consists of several widgets and visually represents indicator and criteria data to compare different simulated scenarios side by side. The indicators vector is mainly based on quantities (e.g. number of victims who died) calculated from a scenario. To be effectively used in a decision support context indicators need to be qualified. Here qualification basically means assigning a level of satisfaction to the indicator data. The "normalised" indicators can be better used as decision criteria. As indicators and criteria data have the same format (vector of scalar values) both can be displayed in the same fashion.

Indicator Table Widget

Fig. 2 shows the normalised indicators of three example scenarios in a tabular form.

Fig. 2.

Criteria Table 1 For reasons of security sensitivity the data used in this paper consist of test data sets that do not reflect reality in any way

Criteria Function Definition Widget

The Criteria Function Definition Widget depicted in Fig. 3 allows the definition of functions converting indicator values to criteria.

Fig. 3. Criteria Function Definition

The view presented in Fig. 4 allows users to correlate individual indicator and criteria values.

Fig. 4. Indicator Scatter Plot

Fig. 5 shows the data as spider charts in order to support the quick assessment of the overall performance of the selected scenarios. In addition a "reference scenario" (in the example L'Aquila (M=7) visualised in orange) can be selected. In this it adds supplemental decision support functionalities. The view is composed of two different widgets: The Decision Strategy Widget and the Decision Ranking Widget.

Decision Strategy Widget

The Decision Strategy Widget allows one to define a weighting strategy for different criteria. In this way, a weighting factor can be assigned to each indicator. This factor scales the contribution of the particular criteria to the overall scenario rank. An additional weighting factor can be selected to weigh criteria in relation to the achieved level of satisfaction. This is done according to the OWA method (see section 3).

Fig. 6. Decision Strategy Definition

Decision Ranking Widget

The Decision Ranking Widget allows the selection of a previously defined decision strategy and criteria function. It applies the selection to the available scenarios and produces a ranking.

Conclusion and Outlook

We have presented a seamless decision support concept to intended connect simulated crisis scenarios and aggregated performance indicators of impact scenarios with state-of-the-art Multi-Criteria Decision Analysis (MCDA) methods. To prove the practicality of the approach we have developed a decision support tool realising the important aspects of the method. The tool effectively supports the decision maker and strategic planner in applying a multi-criteria decision strategy to the ranking of scenarios. The tool is based on state-of the art web technologies and is freely available under an open source license. Currently, the presented approach and DSS are evaluated in a number of different disaster management preparedness case studies in the context of the project.

Future work includes the integration of uncertainty indicators that will allow decision makers to take the inherent uncertainty of scenario data into account. Also, as we do not necessarily know in advance what is important for successful crisis management (what are the relevant indicators). Crisis managers may need to explore what has led to particular indicator values rather than what are the values of particular indicators. We plan to work out a method and the corresponding software enabling to compare the dynamics of scenario evolvement in alternative scenarios.

  (2) an indicator function to map an impact scenario to indicators; (3) a set of representative scenario indicators consisting of aggregated scenario information, e.g. the number of homeless, or the building damage or cost; (4) a criteria function mapping each element of the indicator set to satisfaction; (5) a level of satisfaction in a normalised scale (0-1 or 0%-100%); (6) a ranking function mapping normalized indicator sets to values; and (7) corresponding scalar values (ranks/score).

Fig. 5 .

 5 Fig. 5. Criteria Radar Chart

Fig. 7 .

 7 Fig. 7. Scenario Ranking

adfa, p. 1, 2015. © Springer-Verlag Berlin Heidelberg 2015

Acknowledgements

The research leading to this paper has been performed within the CRISMA project, which is co-funded under the European Community's Seventh Framework Programme FP7/2007 -2013 (grant agreement no. 284552).


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

Oren Deri

Kalev Rannat

Manuel Warum

Chaim Rafalowski

Kuldar Taveter

Peter Kutschera

Merik Meriste

Training Support for Crisis Managers with Elements of Serious Gaming

Keywords: crisis management, training, resource management

This paper presents a methodology and a prototypic software implementation of a simple system supporting resource management training for crisis managers. The application that is presented supports the execution and assessment of a desktop training for decision makers on a tactical and strategic level. It introduces elements of turn-based strategic "serious gaming", with a possibility to roll back in time and re-try new decision paths, while keeping the graphical user interface as simple as possible. Consequently, the development efforts concentrated on: (1) formulating and executing crisis management decisions; (2) assuring responses of all simulated entities adhere to natural laws of the real world; and (3) analyzing progress and final results of the training exercise. The paper presents the lessons learned and discusses the transferability and extensibility of the proposed solution beyond the initial scenario involving accidental release of toxic gas in an urban area in Israel.

Introduction

Organizing realistic trainings which force crisis managers and other training participants to understand that "there is no ideal solution to a complex crisis" is difficult [START_REF] Hawe | Agent-based simulation for largescale emergency response: A survey of usage and implementation[END_REF]. Trainers, trainees and other stakeholders often oversee the obvious mistakes in both, planning of the (field) trainings and assessing the training results. Typical omissions include: the unlimited availability of resources, unrealistically fast arrival of resources at the scene, instantaneous and unfailable performing of some tasks such as a triage, as well as the superhuman endurances of the responders and patients alike.

The CRISMA project team developed a methodology and a set of software tools which can be used to make trainings quite realistic, by keeping most of the training organization unchanged and, where needed, taking into account the peculiarities of the local crisis management organization.

Below we start with a presentation of one concrete use case targeted in the "Accidental pollution" application of the CRISMA project. Thereafter, in section 3, we introduce the technical implementation of the application and section 4 presents the lessons learned in application development and testing, and close with the discussion on transferability and extensibility of the proposed solution.

Decision Support Concept and the Resource Management Training Scenario

The CRISMA project is a European Union funded project focusing on simulation of multi-sectorial large scale crisis scenarios with multi-dimensional effects on the society. According to the CRISMA architecture [START_REF] Dihe | An architecture for integrated crisis management simulation[END_REF][START_REF] Dihe | CRISMA framework architecture v2[END_REF] the aspects of the World we are interested in must be modelled as a set of representative World State data (WSs) which develops in discrete steps (World Transitions), depending on the inherent characteristics of the problem at hand as well as on the users' decisions (Decision Chain). Depending on the selected use case, new WSs may be generated at regular time intervals, following the users decisions (e.g. "evacuate"), or to capture the results of a specific model execution (e.g. "ambulance has arrived to the scene"). In most CRISMA applications, the user can test the effects of alternative decisions. This results in distinct Scenarios, i.e. sets of WSs corresponding to unique Decision Chains.

WSs and whole Scenarios can be compared and assessed using a set of Indicators relevant to a problem at hand. In addition to indicators, CRISMA also supports Criteria -a normalized form of the Indicators which take into account the desired values of the Indicators (e.g. "evacuating the critical patients in less than 30 min is good"). This simplifies the task of analyzing the results and ranking the different Scenarios. However, the Criteria are both subjective and extremely sensitive to small changes in the problem definition. Consequently, the criteria must be defined separately for each training or planning session, e.g. as a part of the training setup.

The CRISMA decision support concept can thus be summarized as: help the users to assess the results of their decisions, but do not suggest a course of action nor impose any particular solutions.

Resource Management Training Scenario

The CRISMA concepts and the software framework have been tested in five pilot applications covering various aspects of the crisis management planning and training [START_REF]CRISMA consortium[END_REF]. Two of these applications target the long-term infrastructure and organizational planning, one concentrates on the development of operational plans, and the final two address the planning, execution and assessment of the resource management trainings.

Here and now we only concentrate on the resource management training application.

During an emergency, commanders in emergency organizations have to decide which resources (ambulances, fireman, police officers, etc.) to deploy, where to send them, and what their tasks should be. In our trainings, the commanders (trainees) are confronted with initial crisis situation and have to express their decisions as they would do during a real accident. Thereafter, the decisions are entered into the system by a steward, the application calculates a new situation, and the result is presented to the trainees. At every step, the trainee can issue additional commands or even step backwards and revise previous decisions (Fig. 1).

Fig. 1. Resource management training workflow

A concrete accident scenario addressed in the resource management training pilot deals with an accidental spilling of chemicals (i.e. bromine) from a container in a major port. The resulting plume poses a threat to a large number of inhabitants in the neighboring city and the severity of the impact depends on the commander's decisions. The main purpose of the application is to assure the impact of the decisions taken by the trainees is realistic, in a sense that the impact is guided by the natural laws and peculiarities of the training setup. For example:

• Resources have to be actively deployed and their arrival to the scene is delayed based on their distance from the scene, type of the road, and weather conditions. • The condition of the patient will deteriorate based of one's previous medical history, exposure to the contamination and the help received (e.g. decontamination). • The condition of resources also changes as result of the interaction between the resource and the environment and/or patient. At some point, resources may become exhausted and cannot perform additional actions.

Concept and Implementation

A CRISMA resource management training application is realized by using six generic "Building Blocks" (BBs) of the CRISMA framework (Fig. 2).

• Integrated Crisis Management Middleware (ICMM) is the core CRISMA middleware service. It acts as a catalogue of the WSs and Scenarios and also supports the work with Indicators and Criteria. Objects of Interest World State Repository (OOI-WS Repository) and the Agents Platform assure that resources of interest for the application are represented in a common form and that their state changes over time, similarly to a way it would be in 'real life'. Main OOIs and activities modeled in the pilot are illustrated in the Fig. 3.

Fig. 2. Functional elements of the resource management training application

Fig. 3. Main Objects of Interest and activities

• Indicator Service calculates the relevant indicators whenever a new World State has been generated by the user or by the agents' platform. Indicators (e.g. "number of dead", "utilization of resources" or "time required for evacuation of critical patients") are used to analyze the progress and final outcome of the training. • Pub/Sub service provides support for event-driven application designs. In this application, all changes to the WS are advertised using the Pub/Sub service. Software elements such as the agents' platform or the indicator service are subscribed to these events and automatically activated as needed. • Finally, the Wirecloud Mashup Platform provides a flexible platform for implementing the application GUI elements and building mashup applications.

The following sections provide more information on three central system components: OOI-WS repository, Agents platform and the Mashup application.

Objects of Interest -World State Repository

OOI-WSR is a service which enables archiving, querying and manipulation the Objects of Interest world state data. It provides a central information exchange and data storage point for all other BBs which consume and manipulate world state data. The repository data is organized in simulation sessions. Each repository session corresponds to a distinct training and consists of multiple world state snapshots. Each of the snapshots represents the state of the world at specific points (in time) and contains the states of all OOIs relevant for the training.

The starting properties of all OOIs in the initial world state (e.g. initial positions and states of citizens and ambulances) are postulated by a trainer. Subsequent world state snapshots are generated by the agents' platform BB and reflect both the inherent model logic and the trainees' decisions.

The OOI-WSR provides an easy http based access to data and enables data manipulations for authorized services. It exposes the stored data in a variety of industry standard such as REST/JSON and OGC WFS/GML in order to support out-of-the-box integration with other web technologies. The repository assures that each OOI is an instance of a specific OOI type representing some real-world resource (e.g. "ambulance"). In order to facilitate data analysis, typed OOIs also prevent the trainers from instantiating the "impossible" resources as a part of the training setup.

OOI Models and Serious Gaming

The general logic and architecture of CRISMA favor the development of strategic turn-based "serious gaming" applications. This type of applications emphasizes making strategic and/or tactical decisions by the players, rather than elaborate graphics or real time reaction skills. A central concept in serious gaming is that of an "agent", which is understood as a proactive and social entity capable of reasoning [START_REF] Hawe | Agent-based simulation for largescale emergency response: A survey of usage and implementation[END_REF][START_REF] Sterling | The Art of Agent-Oriented Modeling[END_REF][START_REF] Miller | Requirements Elicitation and Specification Using the Agent Paradigm: The Case Study of an Aircraft Turnaround Simulator[END_REF][START_REF] Dignum | Agents for games and simulations[END_REF][START_REF] Gentile | An Agent Based Approach to designing Serious Game: the PNPV case study[END_REF].

In our training application, each of the OOI types defined in the OOI-WSR has to be associated with an active representation (agent) of the same OOI. The properties and behavioral models of the software agents corresponding to various OOI types are specified as requirements for the agent-based simulation, using the method put forward in [START_REF] Sterling | The Art of Agent-Oriented Modeling[END_REF][START_REF] Miller | Requirements Elicitation and Specification Using the Agent Paradigm: The Case Study of an Aircraft Turnaround Simulator[END_REF].

The agent-based simulation runs as an autonomous client-server application, using OOI-WSR for obtaining the initial World State (WS0) and recording the consecutive intermediate World States (WS1 … WSn) back to OOI-WSR. The consecutive WSs reflect the evolution of the World and the emerging states for each agent as a result of interactions between different types of agents (for example, AmbulancesPatients) and the impact of the environment (e.g. weather conditions, chemical plume, etc.) on these agents. Our agent-based simulation environment supports behavioral models of the following two types:

1. Behavioral models for each agent type, such as Ambulance and Patient; 2. Scenarios, specifying "what is played" for different games.

Agent types are designed to be as generic as possible, and the sub-typing (e.g. different ambulances) can be achieved through parameterization at the level of the OOI repository. The agents can also communicate with external models via HTTP or some other fixed protocol. This feature simplifies the calculation/estimation of the agent/environment interactions and also makes agent-based simulation models easily expandable, and allows us to solve rather complicated tasks by means of external services.

However, in many cases different games need game-specific agent types because each agent type must support the scenario(s) foreseen for the game by having all the properties and behavioral models that support realization of the game`s scenarios. In addition, the agent types should also be designed to support the calculation of Key Performance Indicators (KPIs) required by the application owner for decision(s) making for a concrete application. Finally, at the level of scenario, each application of the serious game of resource management requires its specific overall agent-based simulation model developed jointly with the application owner.

User Interfaces

At the level of graphical interfaces, the application implements three of the "views" foreseen by the CRISMA architecture (Fig. 4). The views have been realized as web mashup applications running on a Wirecloud Platform. As a consequence, the whole application is built by "wiring" the simpler GUI widgets which are implemented in JavaScript and HTML5. The application runs in any modern web browser as well as many mobile devices, and the further development and improvements can be easily achieved by replacing or adding new widgets and re-wiring the mashup.

Fig. 4. Main User Interface Views

The Pre-training View focuses on the initial creation of a training exercise and allows the user to specify details about the incident from a pre-defined set of templates. This includes the ability to modify each OOI's state and availability. Templates are set up by an expert and already contain a sample configuration of a training exercise but can further be tuned in the pre-training stage. This view is designed to be used by the trainer rather than the trainee.

The Training View allows the trainee to play out decisions such as allocation of resources for the training exercise created during the pre-training step. This is achieved by a turn-based approach where the trainee issues any number of orders and then receives the information on a new situation at discreet time intervals. At any point in time, the trainee can issue new orders if deemed necessary, or go back in time and try alternative approaches.

Finally, the Post-training Analysis View presents the user with indicators that allow measuring and comparing individual outcomes generated during the training exercise. These comparisons render one or more outcomes as a time series to assess how different decisions played a part in the overall progression. Currently, these statistics cover an aggregated view of the health categorization of patients (healthy, injured, severely injured, and dead), how this status compares to the initial health (improved/deteriorated), as well as the resource utilization over time, but additional indicators can be easily implemented using the Indicator Service.

The fact that results are measurable, and can be correlated with decisions is of major importance for the trainees, as it enables evaluation of the decision processes and not just of the results. In this way, the Analysis View can help the trainees to understand the influence of own decisions on the results. The v1 application version already incorporated all the functional elements (Fig. 2), OOIs (Fig. 3) and GUI elements (Fig. 4) which have been introduced in the previous section, but the implementation is still incomplete. In particular, the v2 application will feature: (1) improved patients "life model"; (2) improved interaction between the OOIs (patients, resources) as well as the interaction between OOIs and the environment (e.g. temperature, light conditions, weather, road blocks); and (3) possibility to determine zones with different concentrations and simulate the plume movement during the incident.

Tests of the v1 application with end-users from Magen David Adom (MDA) have confirmed the usability of the application concept and provided a valuable feedback concerning the shortcomings of the design of the graphical interface. For example, the way in which the commands are given to resources in the v1 application differs from the way such commands are given in the command center and the v2 application will be amended accordingly. Nevertheless, all testers agreed that the fully-developed application could enhance the learning experience of the crisis managers' and contribute towards improved preparedness at the MDA. Moreover, the technical development team is confident that the current application design can be easily extended and transferred to new domains and use cases.

Wirecloud offers a solid platform for prototyping and the individual widgets can be easily embedded in other projects at a later time; ICMM simplifies housekeeping and working with indicators and criteria; the Indicator Service simplifies the task of calculating indicators; and the combination of OOI-repository and the Pub/Sub event broker assures that application parts can be developed in parallel without undue overhead.

At the end of the day, the application flexibility remains essentially limited by the functionality of available agent models and by the cost of implementing the missing model features. While no "silver bullet" solution to this issue exists, following features at least mitigate the issue:

• new sub-types of agent models can be introduced in the application by defining a new subtype of OOIs with specific parameters (e.g. max speed, max transport capacity, types of patients which can be treated, etc. for ambulances) within OOI-WSR; and • as agents can communicate with arbitrary external models, existing models can be easily re-used as is or with small changes where appropriate.

One of the interesting opportunities for future development is in supporting the trainer to choose realistic environmental conditions, e.g. by taking into account past situations and/or forecasting new ones.

Another interesting possibility is extending of the software to support the task of defining and testing of the operational plans. Related work is already underway and the results will be presented in a separate paper later on.

4

  Transferability and the Path AheadAt a time of writing the article (October 2014), the first version of the training application has been developed and tested by the end-users, and the work on the second application version is underway. Second version of the application is due in March 2015.

adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011

Acknowledgements

The research leading to this paper has been performed within the CRISMA project, which is co-funded under the European Community's Seventh Framework Program FP7/2007 -2013 (grant agreement no. 284552).

All of the software mentioned in the paper, with the exception of the agents' platform (property of TTU) is licensed under Open Source licenses and can be re-used and extended in other projects.

─ Pub/Sub and WireCloud have been developed in the scope of the EC Future Internet Public Private Partnership initiative, ─ Remaining software has been developed by CRISMA partners AIT Austrian Institute of Technology, Cismet, and NICE, but may be based on or include third party open source products. Full information on all software used and developed within the project is available at the CRISMA catalogue [9].


Markus Stocker
email: markus.stocker@uef.fi

Okko Kauhanen
email: okko.kauhanen@uef.fi

Mikko Hiirsalmi
email: mikko.hiirsalmi@vtt.fi

Janne Saarela
email: janne.saarela@profium.com

Pekka Rossi
email: pekka.rossi@fmi.fi

Mauno Rönkkö
email: mauno.ronkko@uef.fi

Harri Hytönen
email: harri.hytonen@vaisala.com

Ville Kotovirta
email: ville.kotovirta@vtt.fi

Mikko Kolehmainen
email: mikko.kolehmainen@uef.fi

A Software System for the Discovery of Situations Involving Drivers in Storms

Keywords: Environmental knowledge systems, situation theory, ontology, knowledge representation, MMEA, Wavellite

We present an environmental software system that obtains, integrates, and reasons over situational knowledge about natural phenomena and human activity. We focus on storms and driver directions. Radar data for rainfall intensity and Google Directions are used to extract situational knowledge about storms and driver locations along directions, respectively. Situational knowledge about the environment and about human activity is integrated in order to infer situations in which drivers are potentially at higher risk. Awareness of such situations is of obvious interest. We present a prototype user interface that supports adding scheduled driver directions and the visualization of situations in space-time, in particular also those in which drivers are potentially at higher risk. We think that the system supports the claim that the concept of situation is useful for the modelling of information about the environment, including human activity, obtained in environmental monitoring systems. Furthermore, the presented work shows that situational knowledge, represented by heterogeneous systems that share the concept of situation, is relatively straightforward to integrate.

Introduction

Computational models [START_REF] Mulligan | Environmental Modelling: Finding Simplicity in Complexity, chap[END_REF], in particular empirical and physically-based models, can be used on data to obtain information. In some environmental software systems, data is obtained in environmental monitoring and information is about the environment. Data is, specifically, the result of measurement, defined as "the process of empirical, objective, assignment of numbers to properties of objects or events of the real world in such a way as to describe them" [START_REF] Finkelstein | Theory and Philosophy of Measurement[END_REF]. To automatically sample physical properties at high spatiotemporal resolution, measurement is often implemented by environmental sensor networks. An environment monitored in space-time can be abstracted as structured parts of reality, in other words situations [START_REF] Devlin | Logic and Information[END_REF]. The Situation Theory, developed in the 1980s by Barwise and Perry [START_REF] Barwise | Situations and Attitudes[END_REF] and Devlin [START_REF] Devlin | Logic and Information[END_REF], proposes a mathematical ontology for information about situations. A situation is said to support infons, whereby and infon is a tuple consisting of a relation, a set of objects, and the polarity to state whether or not the objects stand in the relation. Environmental phenomena, including humans in activity, are objects. Such objects have attributes and stand in relation to each other.

Models can be used on measurement data to obtain information about situations. The term situation can serve as the most abstract concept in a framework that models information about structured parts of reality monitored in space-time. We present the application of such a framework, called Wavellite, to the modelling of situations with storms and driver locations as the objects. The resulting system reasons over situational knowledge to detect those in which storms and drivers overlap in space-time. Such situations are of interest because people may be at higher risk of accident.

The contribution of this work is two-fold. First, together with our related work [START_REF] Stocker | Representing situational knowledge acquired from sensor data for atmospheric phenomena[END_REF][START_REF] Stocker | Situational knowledge representation for traffic observed by a pavement vibration sensor network[END_REF], we underscore the suitability of the notion of situation for the modelling, i.e. explicit representation, of situational knowledge obtained using models from processed data collected and managed by environmental monitoring systems, possibly building on environmental sensor networks. Second, we present the integration of situations with information about the environment (storms) and situations with information about human activity (driver locations). The application suggests that the notion of situation is suitable not only to model situational knowledge about the natural environment but also situational knowledge about human activity occurring in the built environment. The application arguably supports the claim that to share the concept of situation among heterogeneous environmental monitoring systems facilitates situational knowledge processing, including integration and reasoning.

2

Materials and Methods

The presented system builds on the Measurement Monitoring and Environmental Assessment (MMEA) Platform, 1 the Profium Sense2 semantic technology-based software platform, and Wavellite. 3 The MMEA Platform is a software infrastructure for the collection, storage, processing, and distribution of (processed) observation and forecast data resulting in environmental monitoring. The platform implements an Enterprise Service Bus (ESB) based architecture and was developed within the Finnish MMEA CLEEN/SHOK research program.

In this work, the MMEA Platform handles the collection and processing of data and initiates situational knowledge extraction. The platform also powers the user interface. Profium Sense serves as RDF database for the persistence and retrieval of situational knowledge. Wavellite is a software framework for situation awareness in environmental monitoring. It supports the collection and processing of data, specifically data of environmental sensor networks, and the representation of situational knowledge acquired from data using models, in particular empirical (data-driven) and physically-based environmental models. In this work, Wavellite builds on top of the MMEA Platform, reuses platform functionality and extends it with situational knowledge extraction and representation.

We use radar data for the reflectivity of rainfall intensity in Finland. Radar data is obtained by the Finnish Meteorological Institute (FMI) and is made available by FMI as GeoTIFF encoded images via its Open Data INSPIRE-compliant interface. 4 FMI also provided a MATLAB script that implements an algorithm for the extraction of storm polygon data from radar data. Because the MMEA Platform uses Octave, 5 we implemented the MATLAB program logic as Octave script. Given a scheduled driver direction (consisting of departure time, origin, and destination) we use Google Directions 6 to obtain (alternative) routes, driving distance and estimated driving time.

The MMEA Platform continuously (configurable time interval) retrieves radar data and processes it to extract polygon coordinate data. Polygon data is Well-Known Text (WKT) encoded and wrapped in MMEA observation messages, which are published to the ESB for further processing. We implemented a Wavellite derivation reader, i.e. the component of the Wavellite architecture that reads Wavellite dataset observations, as ESB listener for MMEA observations messages containing storm polygon data. Upon incoming message, the derivation reader converts the MMEA observation message to a Wavellite dataset observation with two component properties, one for time and one for the WKT polygon data. Wavellite dataset observations are then processed by a Wavellite situation engine, i.e. the component of the Wavellite architecture that represents situations with knowledge extracted from dataset observations. For storms, situations 𝑠𝑠 𝑠𝑠𝑠𝑠 support the infon ≪ 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 -𝑎𝑎𝑠𝑠, 𝑠𝑠, 𝑠𝑠 , 𝑙𝑙 ̇, 1 ≫ where 𝑠𝑠, 𝑠𝑠 , and 𝑙𝑙 ̇ are parameters for a storm, a temporal location, and a spatial location, respectively. Given a dataset observation with component properties for spatiotemporal locations it is thus straightforward to map dataset observations to situations by anchoring a new individual (representing the storm) to 𝑠𝑠̇ and the temporal and spatial locations of the dataset observation to 𝑠𝑠 ̇ and 𝑙𝑙 ̇, respectively. The infon states that at time anchored to 𝑠𝑠 ̇ the storm with identifier anchored to 𝑠𝑠̇ covers the spatial region delineated by the polygon anchored to 𝑙𝑙 ̇.

Upon user registration of a scheduled driver direction -consisting of origin, A, destination, B, and departure time -a component of the MMEA platform uses Google Directions to obtain a route, r, between A and B. Given r, the component computes route segments and driver location for each segment. The component uses locations for which there exist situations 𝑠𝑠 𝑠𝑠𝑠𝑠 to segment the route. It is thus temporal locations at which radar images are obtained, or at which storm situations are projected, that determine time steps. For driver locations, situations 𝑠𝑠 𝑑𝑑𝑠𝑠 support the infon

≪ 𝑑𝑑𝑠𝑠𝑑𝑑𝑑𝑑𝑑𝑑𝑠𝑠 -𝑎𝑎𝑠𝑠, 𝑑𝑑 ̇, 𝑠𝑠 , 𝑙𝑙 ̇, 1 ≫
where 𝑑𝑑 ̇ is a parameter for a driver and 𝑠𝑠 , 𝑙𝑙 ̇ are parameters for temporal and spatial locations, respectively. The infon states that at time 𝑠𝑠 ̇ the driver 𝑑𝑑 ̇ is located at 𝑙𝑙 ̇. Users can register driver directions using the prototype Web user interface.

A reasoning component of the MMEA Platform uses situations 𝑠𝑠 𝑠𝑠𝑠𝑠 and 𝑠𝑠 𝑑𝑑𝑠𝑠 to discover situations 𝑠𝑠 ℎ𝑟𝑟 that support the infon ≪ higher -risk, 𝑑𝑑 ̇, 𝑠𝑠 , 𝑙𝑙 ̇, 1 ≫ which states that the driver 𝑑𝑑 ̇ is in a higher risk situation at temporal and spatial locations 𝑠𝑠 ̇ and 𝑙𝑙 ̇, respectively. The parameter 𝑙𝑙 ̇ for spatial location is optional in this infon, as it can be obtained from the situation with driver-at-relation infon with same objects anchored to parameters 𝑑𝑑 ̇ and 𝑠𝑠 . Situations 𝑠𝑠 ℎ𝑟𝑟 are of interest to drivers.

The discovery of such situations is implemented as SPARQL [START_REF] Prud'hommeaux | SPARQL Query Language for RDF[END_REF] queries that leverage on Profium Sense support for quantitative spatial reasoning and qualitative spatial relations in SPARQL queries. Specifically, the query is for situations with stormat-relation infon and situations with driver-at-relation infon with same temporal locations and driver spatial location inside storm spatial location.

Of particular interest in this system is situation projection. The projection of situations 𝑠𝑠 𝑑𝑑𝑠𝑠 is trivial, as it amounts to the registration of a point of departure at some future temporal location. The projection of situations 𝑠𝑠 𝑠𝑠𝑠𝑠 is, however, more complex. Indeed, the task amounts to forecasting the future spatial location of storms. As a proof of concept, we have implemented Octave program logic to support forecasting storms. The method uses temporal clustering [START_REF] Ester | A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[END_REF] and recursively searches for matching storms in consecutive radar images to form storm tracks over time. At each time step, a storm has no predecessors if it just formed; one predecessor if it has a defined path from the previous time step; more than one predecessor if several storms have merged together since the previous time step; or it disappeared. A storm can progress in one direction or multiple directions, if it has split into different storms [START_REF] Dixon | TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting-A Radar-based Methodology[END_REF]. For each storm track, the method then fits a suitable model to observed storm centroids and projects the storm using the model. The weighted linear regression model assigns smaller weights to older observations in order to make projection more sensitive to newer observations. The direction vector of the storm centroid is used to displace all storm polygon vertices.

The Web graphical user interface is a JavaScript client that makes use of OpenLayers,7 jQuery, 8 Google Direction Service, 9 and a Web service for communication with the MMEA Platform (retrieval of situations and addition of directions). In Wavellite, situations are individuals, instances of the class Situation. The class is defined in the Situation Theory Ontology (STO) [START_REF] Kokar | Ontology-based situation awareness[END_REF] and reflects Situation Theory semantics. Thus, Wavellite builds on Semantic Web technologies, in particular the Resource Description Framework (RDF) [START_REF] Manola | RDF Primer. W3C Recommendation[END_REF] and the Web Ontology Language (OWL 2) [START_REF]W3C OWL Working Group: OWL 2 Web Ontology Language Document Overview (Second Edition)[END_REF]. Wavellite interacts with Profium Sense to store and retrieve RDF.

Discussion

The system operates in near real-time and populates the RDF database with situational knowledge, which can be visualized. Figures 1 and2 provide examples. Upon user registration of an origin (e.g. Kuopio) and a destination (e.g. Tampere) for a scheduled driver direction starting at temporal location 𝑠𝑠 𝑙𝑙 , the system segments the route and computes situations 𝑠𝑠 𝑑𝑑𝑠𝑠 . Given situations 𝑠𝑠 𝑑𝑑𝑠𝑠 and 𝑠𝑠 𝑠𝑠𝑠𝑠 , we can create visualizations such as the map shown in Figure 1 where the driver location (circle) is shown along the direction between Kuopio and Tampere and the spatial location of the storm is shown as polygon. The system reasons over such situational knowledge to discover higher-risk situations in which the driver location and storm location overlap in space-time. Figure 2 visualizes several higher-risk situations. Drivers may want to be aware of such situations.

Figure 2 shows situations at three time steps on October 10, 2014: at 14:15, Figure 2(a); at 15:55, Figure 2(b); and at 17:00, Figure 2(c). The relatively large storm is moving south-west to north-east from Turku to Kuopio. At the same time, several drivers are located along directions, Turku-Kuopio, Kuopio-Tampere, and Tampere-Helsinki. At various times, drivers overlap the storm. Only the driver directed from Tampere to Helsinki is never in a higher-risk situation.

The presented software system is a proof of concept. The aim was to demonstrate the integration of situational knowledge obtained from heterogeneous data sources using a set of technologies, including the MMEA Platform, in an environmental monitoring context. However, with further development the system could mature so that it can be deployed for active use.

Drivers are perhaps the obvious potential interest group for such a system. Directions scheduled in a calendar could be automatically 'safety checked' hours prior to departure. The system could inform drivers accordingly. For end consumers, such as drivers, mobile applications may be of particular interest. The presented Web graphical user interface is a lightweight JavaScript application. It communicates via HTTP requests with map, direction, and situation services, which implement the bulk of the program logic. Thus, developing a graphical user interface for mobile platforms should be fairly trivial.

The current system can also be extended to integrate more situational knowledge, and thus improve situation awareness [START_REF] Endsley | Toward a theory of situation awareness in dynamic systems[END_REF]. For instance, during winter road conditions are critical. In addition to snow storms, of particular interest is situational knowledge for road sections that may be covered with ice, of poor visibility, or congested. Such information is likely to result from data processed by different systems. However, because of the shared ontology, the integration of situational knowledge would be as straightforward as in the case presented here.

Google Directions suggests alternative directions. This feature can be exploited when higher-risk situations are discovered. For such situations, the system can notify drivers and suggest (1) alternative directions with same departure time, (2) same direction with earlier or later departure time, or (3) to postpone travelling altogether if no direction can be scheduled for which the driver will not find herself involved in a higher-risk situation.

Critical for future work, in particular work that aims at a mature system deployable for active use, is the evaluation of situation projection. This can be achieved through analysis of how well forecasts agree with observations, which requires the construction of a dataset that appropriately aligns storms in space-time. Of particular interest is the comparison of storm polygon centroids and storm spatial extent. Uncertainties related to storm development could be modelled explicitly. Instead of projecting storms along a line, or path, a system that takes uncertainties into account could project storms along regions that widen the further the projection is in space-time. Rather than to search for higher-risk situations along a line, the system could thus search for such situations in the region in which a storm will likely be. Naturally, such a search is computationally more demanding. Further issues need to be addressed in work that aims at a system deployed for active use, including liability in case of system failure. Such concerns are however beyond the scope of this paper.

Route planning is an old problem and algorithms have been developed that are constraint aware. For instance, authors have proposed estimating the cost of travelling through particular grid cells, whereby factors such as weather conditions can influence the cost value [START_REF] Szczerba | Robust algorithm for real-time route planning[END_REF]. In [START_REF] Balke | A situation-aware mobile traffic information system[END_REF], the authors present a personalized mobile traffic information system capable of handling weather information, including rainfall.

There exist online services for road trip planning that include weather information. For instance, AccuWeather 10 supports this feature. Together with the direction, the service shows (forecast) weather at various locations along the direction. The Weather Channel 11 offers a similar feature. Aside the obvious difference between weather and storms as well as the nature of the data involved, the main novelty here is arguably in technologies that support the explicit representation of situational knowledge with machine readable and interpretable semantics specified in ontologies that can be shared among heterogeneous environmental monitoring systems.

Conclusion

We presented an environmental software system that obtains, integrates, and reasons over situational knowledge about storms and driver directions. As such, the system demonstrates the integration of situational knowledge about natural phenomena and situational knowledge about human activity. All knowledge is represented explicitly. As a consequence, the system can manipulate situational knowledge. Spatio-temporal situation reasoning is an example for such manipulation. As we demonstrated, inferring situations in which drivers are at higher risk can be achieved by means of a simple SPARQL query. The work underscores the suitability of the concept of situation, as defined in Situation Theory, for the modelling of situational knowledge obtained using computational models from processed data collected and managed by environmental monitoring systems.

A thorough discussion of the advantages and disadvantages of the presented system could be valuable. However, such an evaluation requires at least one comparable system that uses a different set of technologies. We currently do not have such a comparable system. Aside from the discussed advantages, a disadvantage of the presented system may be the potentially large number of generated RDF statements. However, such concerns need to be addressed primarily by the RDF database.

Naturally, the described system could be extended to include situations of various other types that are of interest to driver Situation Awareness [START_REF] Endsley | Toward a theory of situation awareness in dynamic systems[END_REF][START_REF] Stanton | Distributed situation awareness in dynamic systems: theoretical development and application of an ergonomics methodology[END_REF]. For instance, situations involving road sections possibly covered with ice may be of interest. It is trivial to see how such additional situational knowledge can be integrated in the present system. Other directions for future work include the development of mobile applications and the integration with driver assistance systems.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Overview of the Web graphical user interface. Situations are shown animated on a map according to simulation parameters (start and end time). On the right hand side, new directions can be added. Visible are situations at 17:50, including a large storm and the location of a driver (circle) on a direction from Turku to Kuopio. The direction is highlighted.

adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011

http://mmea.fi/

http://www.profium.com/en/profium-sense

http://www.uef.fi/en/envi/projects/wavellite

https://en.ilmatieteenlaitos.fi/open-data

https://www.gnu.org/software/octave/

https://developers.google.com/maps/documentation/directions/

http://openlayers.org/

http://jquery.com/

https://developers.google.com/maps/documentation/javascript/directions

http://www.accuweather.com/en/driving-directions-weather

http://www.weather.com/travel/trip-planner

Acknowledgements

We thank the Finnish Funding Agency for Technology and Innovation (TEKES) and the Academy of Finland for funding this research. This work has been carried out in TEKES funded CLEEN SHOK programme "Measurement, Monitoring and environmental Assessment" (MMEA, decision number 427/10) and in the Pathway project funded by the Academy of Finland.