Nina Nikolova

Kalina Radeva
email: kalinad@abv.bg

Data Processing for Assessment of Meteorological and Hydrological Drought

Keywords: Drought, Precipitation, River Runoff, SPI, SDI

Accurate and reliable data processing is of primary importance for drought assessment. It helps decision makers to lay out mitigation measures within the context of drought preparedness planning and water resources management. In order to understand meteorological and hydrological drought, we need to identify drought characteristics (duration, severity and spatial extent). Drought indices are essential tools quantifying drought severity and identifying its frequency and duration. For the calculation of drought indices, availability of long time series of undisturbed, good-quality observational data is essential. The studied area cover a Bulgarian part of the catchment of Struma River which is one of the largest Bulgarian rivers. The general aim of this research is to evaluate the occurrence of hydrological and meteorological droughts in Struma River basin and to show utilization of various indices for comparative analysis of meteorological and hydrological drought. Drought events are identified using the following indices -Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Streamflow Drought Index (SDI) for time scales 6 and 12 months. Additionally to these indices, we use also Rainfall Anomaly Index (RAI) and introduce Streamflow Anomaly Index (SAI). The main investigated period is 1962 -2016.

Introduction

Drought is a major natural hazard with multiform impacts on the environment, the economy and society. Drought affects more regions and more people globally than many other natural hazards [1]. The American Meteorological Society have grouped various kind of drought into four categories: meteorological, agricultural, hydrological and socioeconomic droughts. The four categories are associated with different components of the hydrologic cycle. Generally, precipitation is the driving and critical factor in the hydrologic cycle. Meteorological drought is an extreme weather phenomenon having the character of an atmospheric anomaly caused by a period of below-normal rainfall. Disruption of the water balance of a given area due to a shortage of rainfall and strong evapotranspiration (meteorological drought) consequently causes excessive drying of the soil (soil drought), lowering the groundwater level and reducing water flows in the rivers (hydrological drought). Among the different types of droughts, the hydrological component is the most important, given the high dependence of many activities (industrial, urban water supply and hydropower generation), on the surface water resources [2]. Hydrological droughts also have a significant impact on water quality by disturbing the river's natural processes of self-purification. Hydrologically, the region impacted by drought is not only limited to the river network and its vicinity, but also to the whole basin [3]. Reasons for the occurrence of hydrological drought are complex, because they are dependent not only on the atmosphere, but also on the hydrological processes that feed moisture to the atmosphere and cause storage of water and runoff to streams [4]. According to Van-loon [5], hydrological drought has the most significant effects in almost all different sectors as shown in Table 1. Efficient early warning system against drought and integrated water resource management requires proper interpretation of area monitoring data at the drainage-basin scale. The success of drought preparedness planning and mitigation depends, in particular, on how accurate the droughts are defined and drought characteristics are quantified. Therefore it is important to investigate both -hydrological and meteorological droughts in the river basin. The number of droughts will increase on a global basis due to global warming which leads to higher temperatures and favors dry conditions [6,7]. In the European Union (EU), more than 4800 drought-related impacts entries have been identified in the European Drought Impact Report Inventory (EDII) across 15 different impact categories from agriculture to water quality [8] and financial losses over the last 3 decades were estimated to over EUR 100 billion [9]. Climate change studies project longer, more frequent and severe meteorological droughts in southern Europe, especially in the Mediterranean and the Balkans including Bulgaria [10]. Both, geographic location and climatic conditions are favorable to the occurrence of droughts in Bulgaria. The country has experienced several drought episodes during the 20th century, most notably in the 1940s and 1980s. Drought in Bulgaria was most severe in 1945 and especially in the year 2000, with precipitation 30% less than the current climatic values [11]. Studies revealed that there is a general tendency towards drying during the last two decades in some parts of Bulgaria [12].

To counteract the negative effects of drought and to take appropriate action and preventive measures, reliable and proven indicators of the intensity of drought should be available. Drought indices are very important tools to monitor and to assess drought [13]. Although a multitude of drought indices exist, we select a subset of three indices to evaluate different types of drought: the Standardized Precipitation Index (SPI) [14], the Standardized Precipitation-Evapotranspiration Index (SPEI) [15,16]; [17], the Streamflow Drought Index (SDI). A significant part of the drought indicators, described in the literature and used in the monitoring of drought in various regions of the world, is based on the amount of atmospheric precipitation. These include the Standardized Precipitation Index (SPI), recommended for use in the practical monitoring of drought [14,[START_REF]River basin management plan (Struma River), 2010-2015 (PURB)[END_REF][START_REF] Keyantash | The Quantification of Drought: An Evaluation of Drought Indices[END_REF][START_REF] Edwards | Characteristics of 20th century drought in the United States at multiple time scales[END_REF]. SPI is used to detect periods of drought and assess its severity. It is used in the USA (for operational monitoring of drought by the National Center for Drought Prevention), and in Europe, among others in Germany [START_REF] Olukayode Oladipo | A comparative performance analysis of three meteorological drought indices[END_REF], Greece [START_REF] Nikolova | Variability of Summer-Time Precipitation in Danube Plain, Bulgaria[END_REF], Portugal [START_REF] Hänsel | Drought in a Changing Climate, Saxon Dry Periods. Bioclimatological Conference[END_REF], Poland [START_REF] Hänsel | The Modified Rainfall Anomaly Index (mRAI)-is this an alternative to the Standardised Precipitation Index (SPI) in evaluating future extreme precipitation characteristics?[END_REF] and Bulgaria [START_REF] Fluixá-Sanmartín | Searching for the optimal drought index and time scale combination to detect drought: a case study from the lower Jinsha River Basin, China[END_REF]26,27]. Another index recommended by WMO is the Standardized Precipitation-Evapotranspiration Index (SPEI). The original SPEI input parameters are precipitation and temperature data. Mathematically, it is similar to SPI, but it includes the effect of temperature variability. The SDI index has not been used and studied in Bulgaria, but it was applied to the analysis of regional droughts in Europe [28,29], Asia [30,31,32], Turkey [33] and USA [34].

The main purpose of this study is to determine the frequency of occurrence of meteorological and hydrological drought in different periods over many years , based on the Standardized precipitation index (SPI), Streamflow drought index (SDI) and additionally Standardized precipitation evapotranspiration index (SPEI) and comparison of these indicators as criteria for assessing drought.

Study Area and Data

The trans-boundary Struma River basin has a total area of 16 747 km 2 Annual precipitation values have ranged from 480 mm (in lower and southernmost parts, station Sandanski) to mm to 700 mm at the altitude higher than 1100 m (station Rila monastery).

The mean annual flow of the Struma River is 2 242 × 10 6 m 3 , which constitutes 13% of the country's total precipitation and runoff with the coefficient of variation Cv = 0.32 and Cs = 0.57. The internal variability of river runoff depends on combinations and distribution of rainfall, snow cover and air temperature. In the high mountain parts of the area, the river flow in the winter is low, with a minimum in February, as a result of the reduced water flow under the thick snow cover and low temperatures. In this part, heavy rainfall in the spring and the presence of intense snowmelt led to high water period with a peak in May (up to about 30% of the annual runoff). With the decrease of the altitude of the basins high water period shifts to the winter months, the maximum in the southernmost parts of the basin is in February. The low water period begins in July and continues until winter, with monthly minimum in September.

The Bulgarian part of the River Basin is home to 485 000 people (7 % of Bulgaria's total population). The main water users of surface water are domestic supply, livestock farming, industry, energy production and to a less degree irrigation [35]. The runoff variability is analysed on the bases of monthly data from nine stream gauging stations (see Table 2).

Table 2. List of stream gauging stations used in the research

The information about the precipitation is from six meteorological stations situated close to the stream gauging stations in an area with different geographical and climatic conditions (see with the geographical position of stream gaging and meteorological stations is represented on Figure 1. Standardized Precipitation Evapotranspiration Index (SPEI.) is a relatively new drought index. The study of drought by SPEI in Bulgaria is still not well developed. SPEI uses the basis of SPI but includes a temperature component, allowing the index to account for the effect of temperature on drought development through a basic water balance calculation [15]. The multi-scalar character of the SPEI enables its use in vari-ous scientific disciplines to detect, monitor and analyze droughts [15]. SPEI can measure drought severity according to its intensity and duration and can identify the beginning and the end of drought periods. It can be calculated for time steps of as little as 1 month up to 48 months or more. Monthly updates allow practical application, and the reliability of the results increases with the increasing of the available time-series data.

Streamflow Drought Index (SDI) is a very simple and effective index for hydrological droughts [28]. The SDI for each gauged station was determined using the following relation:

𝑉 𝑖,𝑘 = ∑ 𝑄 𝑖,𝑗 3𝑘 𝑗=1 𝑖 = 1,2 … 𝑗 = 1,2 … , 12 𝑘 (1) = 1,2,3,4
in which Vi,k is the cumulative streamflow volume for the i-th hydrological year and the k-th reference period, k = 1 for November-January, k = 2 for November-April, k = 3 for November-July, and k = 4 for November-October. Based on the cumulative streamflow volumes "Vi,k", the Streamflow Drought Index (SDI) is defined for each reference period k of the i-th hydrological year as follows:

𝑆𝐷𝐼 𝑖,𝑘 = 𝑉 𝑖,𝑘 -𝑉 𝑘 𝑆 𝑘 𝑖 = 1,2 … , 𝑘 = 1,2,3,4 (2)
where "Vk" and "Sk" are respectively the mean and the standard deviation of cumulative streamflow volumes of the reference period "k" as these are estimated over a long period of time. In this definition the truncation level is set to "Vk" although other values based on rational criteria could be also used. Nalbantis and Tsakiris [28] quantify 4 states (classes) of hydrological drought for SDI, which are determined in an identical way to those used in the meteorological drought indices SPI and SPEI. States of drought are defined by an integer number using criteria as per Table 3. In the proposed methodology, the reference periods start from November of each year, which is considered the beginning of hydrological year in Bulgaria. The drought assessment is made using two overlapping periods: at annual level -hydrological year (November -September) and at seasonal levelcold half-year (November -April). In order to evaluate the drought during these two periods SPI, SPEI and SDI are calculated with a 12 and 6 month step, respectively.

The present paper aims to show the utilization of various indices for drought detection and analysis. Additionally to the above mentioned indices, we calculate also Rainfall Anomaly Index (RAI) which can be used for assigning the magnitudes to positive and negative precipitation anomalies. For analysis of negative anomalies and drought events the index is calculated by

𝑅𝐴𝐼 = -3 𝑃 𝑖 -𝑃 ̅ 𝐸-𝐸 ̅ ̅̅̅̅̅̅ (3)
where Pi is precipitation for every year, P -average e precipitation for the investigated period, and E is average of ten lowest annual precipitation totals (driest years) for the investigated period.

RAI is used as a tool for meteorological drought investigation by Olukayode Oladipo The same method was applied to annual streamflow data and Streamflow Anomaly Index (SAI) was calculated. The classification of dry periods according to RAI and SAI is shown in Table 4.

Results

The SPI, SPEI and SDI values based on the meteorological and hydrological data from the Struma River Basin were calculated for the 6 and 12 month time scales during 1962-2016. We investigate hydrological year from November to October. Due to the climatic conditions in South Bulgaria, where a minimal rain period lasts from June to October, the SPI and SDI values of 6 months and above time scale step seem more useful than the 3 months one. The first period (November through April) includes the high-flow period of the rivers in the investigated area. The distributions of drought indices (SPI, SPEI and SDI) for the period November-April are provided in Figures 2. The SPI distributions demonstrate that some stations exceed the threshold of classes for extreme drought (SPI < -2) during the years 1993 and 2001, when SDI indicates moderate drought (SDI < -1,5). There is a good synchronicity between the SPI and SPEI. Small differences have been detected in separate cases in relation to drought severity only. However SPEI confirms the occurrence of meteorological drought established by SPI. The indexes showed that there was a significant drought problem during the years 1989-1996 and 2000-2002, in the middle and the south part of the basin.

According to SDI-6 for November -April, hydrological drought is observed in about 50 to 60 % of the years in the investigated period. On the other side, the precipitation data and SPI-6 shows, that meteorological drought for the winter-time is manifested in about 40 to 50% of the investigated years, but the severity is often higher than for the hydrological drought.

The distribution between mild, moderate, severe and extreme drought events at the different stations for the winter-time (November -April) for all the years is presented on Figure 3. Mild drought was the dominant drought state in all of the stations. Further results based on SDI do not show any extreme drought events as compared to the SPI index which shows extreme drought occurrences (SPI ≤ -2.0) in three stations (Rila monastery, Rila, Sandanski) located in mountainous and south part of the basin. In the second step, the analysis of the indices SPI, SPEI and SDI 12 months' time scale has been executed in order to evaluate the long-term drought episodes. The most widespread extreme 12-month drought occurs, according to SPI, in 1993. SPEI also shows drought occurrence in 1993, but the severity of the drought is lower than the one detected by SPI. On the other side, SPEI detects severe and extreme drought in 2000 and 2001 while SPI shows mainly, moderate drought (see Fig. 4).

Sequences of dry periods (SPI, SPEI and SDI < -1,5) took place in 1983-1985, 1992-1995, 2000-2002, and for SDI, also in 2011-2014 (see Fig. 4). The severest meteorological drought among all the stations was detected in mountainous part of the basin i.e. Rila monastery and Rila stations. The year 1993 was identified as the driest year in terms of drought severity, as extremely dry (SPI value -4.12). The years 2000 (-2.83) and 2001 (-2.91) also were characterized as Extremely Dry. The longest hydrological droughts occurred in the periods 1992-1995 with a peak intensity of -1.83 (severe drought), and a mean intensity of -1.17, and in 2000-2002 with peak intensity -2.114 (extreme drought) and a mean intensity of -1.13.

Mild drought was the dominant drought intensity in all the stations (see Table 5.

). An average of 65.68 % for SPI and 64% for SDI of mild drought occurred in the investigated area. Moderate drought intensities occurred at an average of 17.1% for SPI and 23% for SDI. Severe drought occurred at an average of 8.8% for SPI and 13% for SDI, respectively. According to SPI results extreme drought intensity is 8.2%. There are only two cases of extreme drought in Struma River basin within this period according to SDI. Both indices SPI and SPEI show that at annual scale the drought is widespread in 1993 and 2000, when the drought is observed in all of the investigated stations, and in 2011 (the drought is observed in about 80-85% of investigated stations).

SPI and SPEI as well as SDI show great difference for the years 1984 and 1990. According SPI the year 1984 was dry in about 60% of investigated stations. On the other hand, the SPEI shows drought only in 20% of the stations. This can be explained by comparatively low values of air temperatures in 1984. Due to high temperatures in 1990, SPEI shows dry condition in all of the investigated stations and this was not the case when only precipitation (SPI) was considered.

Annual values of Rainfall Anomaly Index and Streamflow Anomaly Index are in coincidence with the results from the drought analysis by SPI and SDI. According to The good synchronicity between RAI and SAI, as well as the results of SPI and SDI, allow us to conclude that SAI could be used for hydrological drought investigation. The advantage of RAI and SAI is the simplicity of the calculation and the fact that they are based only on the data for precipitation or streamflow respectively. According to Olukayode Oladipo [38] there is a negligible difference between RAI and the more complicated Palmer drought index. Hänsel et al. [41] suggest modification in the calculation of RAI and the use median instead of average and find high correlation with SPI. The utilization of RAI for drought analysis is pointed out by Fluixá-Sanmartín [42] who compare various indices as SPI, RAI, and percent of normal (PN)

Conclusion

Drought assessment indicators allow for easy interpretation of results and comparison of regions with different climatic conditions. The SPI and RAI are preferred for drought investigation due to easy provision with source data (precipitation only) and simplicity of the calculations. The same advantages have SDI and SAI for hydrological drought analysis. On other side, the indicators based only on climatic factors do not fully account for the drought development process. The use of only one element in the form of precipitation as input data (SPI) reflects in principle only one phase -atmospheric drought. The introduction of a second factor, as air temperature (SPIE), gives a more complete picture of the meteorological and humidity conditions of the area. The drought assessment using indicators based on flow data is a summary of the development of the drought process. According to the results most of the investigated area is drought-prone and it should be a priority of regional water management projects focusing on drought mitigation.

The SPI extreme drought category on the 12-month time scale occurs most frequently in the middle part of the basin. Similarly, results based on hydrological drought analysis shows that the severest drought events occurred in the upper and middle part of the basin during 1994 -1993, 2000-2001 and 2007-2008. In most of the investigated areas the cases of mild and moderate drought increased, particularly over the last two decades .

The SDI analysis results in this work show that significant drought characteristics can be found in analysis periods of six months (November to April) and twelve months (November to October). The results based on SPI also suggested that the drought severity is very critical in the mountainous areas, which in general, are more vulnerable to drought phenomenon rather than other parts of the country Calculating Data Driven Drought Indices for the Struma River Basin provides the foundation to evaluate hydrometeorological drought and to serve as preliminary assessment of the risk of drought events in other drought exposed basins.

Fig. 1 .

 1 Fig. 1. Studied area and location of stream gauging and meteorological stations

  [38], Keyantash and Dracup [36], Nikolova and Vassilev [39], Hänsel and Matschullat [40], Hänsel et al.[41], Fluixá-Sanmartín et al. [42] etc.

Fig. 2 .

 2 Fig. 2. Chronological distribution of drought indices for the period November -April

Fig. 3 .

 3 Fig. 3. Comparison of percentage of drought severity categories -6 -month time step (November-April) of (a) SPI (b) SDI

Fig. 4 .

 4 Fig. 4. Chronological distribution of drought indices for hydrological year (November -October)

Table 1 .

 1 The major drought impacts for different drought categories

	Impact category	Impact sub-category	Hydro-logical drought	Meteoro-logical drought	Agricultural/ soil moisture drought
	Agriculture	Rain-fed	x	x	x
		Irrigation			x
	River basins/ecosys-	Terrestrial	x	x
	tems	Cooling	x
	Energy and industry	Hydro-power	x
	Navigation		x
	Drinking water		x
	Recreation		x
	Source: Van-loon (2015)

  The basin has a pronounced mountainous character sculpted by the hydrographic network and glacial denudation, with an average elevation of about 900 m above sea level. The climate in the Upper Struma River is moderate continental, while in the middle part -transitional continental with significant Mediterranean influence. Mean annual precipitation in the basin is 566 mm and potential evapotranspiration is 629 mm. Most precipitation is concentrated in the winter months, with peak rainfall occurring from November through March and the summer is quite a dry season[27].

	and is the second
	largest catchment area in Bulgaria and the fifth longest Bulgarian river. Its basin is
	shared by four countries: Bulgaria (50.6%, 8,473 km 2 ), Greece (35.8%, 5,990 km 2 ),
	FYROM (9.8%, 1 641 km2) and Serbia (3.8%, 643 km

2

). The total length of the river is about 390 km. It springs from the Southern slopes of the Vitosha Mountain, in Bulgaria, (2180 m a.m.s.l.) and ends up in Aegean Sea (Strymonikos Gulf). After a southsoutheast route of 290 km, Struma River leaves the Bulgarian territory near the Kulata village (85 m a.m.s.l.).

Table 2 )

 2 . The main investigated period is 1962 -2016. The studied area

	River	Stream gauging station	Drainage area (km 2 )	Average eleva-tion of drain-age area (m)	Meteorological station	Altitude (m)
	Struma	Pernik	284.0	1018	Pernik	768
	Struma	Boboshevo	4320	974
	Sovolianska	Garlyano	41.90	1682	Kyustendil	520
	Bistrica
	Eleshnica	Vaksevo	315.3	1058
	Rilska	Pastra	222.0	1918	Rila monastery	1150
	Bistritsa	Slavovo	105.0	-	Rila	505
	(Blagoevgrad)
	Biistritsa	Blagoevgra	206.5	1467	Blagoevgrad	424
	(Blagoevgrad)	d
	Sushitska	Polena	32.10	-
	Sandanska	Lilyanovo	118.4	-	Sandanski	206
	Bistrica

Table 3 .

 3 Classification of drought conditions according to the SDI, SPEI and SPI

	State	Description	Criterion
	1	Extremely Wet	SDI;SPI;SPEI ≥2.0
	2	Very Wet	1.5 ≤ SDI;SP;SPEI I ≤ 1.99
	3	Moderately Wet	1.0 ≤ SDI;SPI;SPEI ≤ 1.49
	0	Non-drought	SDI;SPI;SPEI ≥ 0
	-1	Mild drought	-1.0 ≤ SDI;SPI;SPEI ≤ 0.0
	-2	Moderate drought	-1.5 ≤ SDI;SPI;SPEI ≤ -1.0
	-3	Severe drought	-2.0 ≤ SDI;SPI;SPEI ≤ -1.5
	-4	Extreme drought	SDI;SPI;SPEI ≤ -2.0

Table 4 .

 4 Classification of droughts according to Rainfall Anomaly Index and

	Streamflow Anomaly Index
	RAI / SAI	Classification
	-0.49 to 0.49	Near normal
	-0.99 to -0.50	Slightly dry
	-1.99 to -1.00	Moderately dry
	-2.99 to -2.00	Very dry
	≤-3.00	Extremely dry

Table 5 .

 5 Number of years and percentage (%) occurrence of various degrees of drought in each of the stations according toSDI 12

	Stations		States of drought
		Mild	Moderate	Severe	Extreme
	Struma -Pernik	13 (50)	10 (38.46)	2 (7.69)	1 (3.85)
	Struma -Boboshevo	16 (64)	5 (20)	4 (16)	0
	Eleshnica-Vaksevo	20 (69)	7 (24.1)	1 (3.45)	1 (3.45)
	Sushicka-Polena	15 (60)	7 (28)	3 (12)	0
	Sovolqnska Bistrica	20 (69)	6 (20.7)	3 (10.3)	0
	Blagoevgradska
	Bistrica 51470	16 (72.7)	3 (13.65)	3 (13.65)	0
	Blagoevgradska
	Bistrica 51480	12 (60)	4 (20)	4 (20)	0
	Sand. Bistrica -
	Lilianovo	13 (65)	4 (20)	3 (15)	0
	Rilska -Pastra	14 (63.6)	5 (22.8)	3 (13.6)	0


Rahmat Benny Benyamin Nasution
email: benny.nasution@polmed.ac.id

Widia Sembiring

Muhammad Syahruddin

Nursiah Mustari

Abdul Rahman Dalimunthe

Nisfan Bahri

Bertha Br Ginting

Zulkifli Lubis

Weather Data Handlings for Tornado Recognition using mHGN

Keywords: Graph Neuron, Hierarchical Graph Neuron, Multidimensional Hierarchical Graph Neuron, Natural Disaster Forecast, Tornado Forecast X, XXXXX, XX, XXX, XXXXXXXXX, XXX, X, XXXXX, XXXX

The usage of the mHGN as a pattern recognizer cannot necessarily be used to recognize tornados. Two important issues that need to be solved first are related to data handlings of not-accurately recorded data, and to those of complex weather data. The not-so-appropriate data handlings will produce high false positive and true negative rate of the recognition results. Yet, the latest development of those data handlings has been carried out, and has shown positive and promising results. Such a new approach of data handlings can, therefore, be used to improve the quality and the accuracy of forecasting a tornado. The results taken from a simulated circumstances of a multidimensional pattern recognition have shown, that the tornado can be recognized around 9 hours earlier with 90% of accuracy. However, several improvements related to the data representation within the mHGN architecture need to be implemented. The deployment of mHGN in several risky areas of tornados can then be expected as an alternative way of reducing damages, losses, and costs.

Introduction

The latest storms that hit several countries across the gulf part of American continent have given a clear evidence that natural disasters are real, and they will occur regularly.

Although the upcoming of a storm can be monitored and people are usually notified many hours before it arrives, damages, injures, and casualties are usually still involved. The reason to this is maybe because not only the upcoming is important, but also the lack of preparation of people contributes to those damages and casualties. The clear evidence to this is the two tsunamis: in Indonesia in 2004, and in Japan in 2011. The situation is worse when the disaster is a tornado, because it occurs without an adequate notification (siren, alarm, or radio announcement). In the US [START_REF] Cheng | A Nowcast Model for Tides and Tidal Currents in San Francisco Bay, California[END_REF] [2] the number of occurrences of a tornado is the highest compared to those in other countries.

The reason why the occurrence of natural disasters cannot be forecasted is due to its randomness. Furthermore, many people are not aware of the precondition of its occurrence, and they are not prepared to face that. Such a randomness is not the case of a tornado. In the US, tornados are not random. Most of tornados occurred between April and June. The area of occurrences is in the southeast area of the US. Nevertheless, every single tornado appears very suddenly. Prior to its appearance there is no sign that can be recognized beforehand, for instance at least 10 hours earlier, so that people have adequate time to get away of it. As the result, damages, losses, and costs totally cannot be predicted.

Researchers have worked on handling natural disasters. The general term that they have developed is called disaster management. The management deals with prior, while, and post disaster. The most difficult part dealing with natural disaster, such as a tornado, is to forecast it. It is true that some kind of early warning system has been built in many countries. In the US, a siren is started when the system detects an upcoming of a tornado. However, the time distance between the siren and the occurrence of tornado is very short. It is usually less than 30 minutes. Such period of time is inadequate for people to protect themselves from the fierce of tornado's force. For centuries researchers have worked on discovering ways to forecast the upcoming of a natural disaster. Some of them are still at the stage of now-casting [START_REF] Kirschbaum | Advances in landslide nowcasting: evaluation of a global and regional modeling approach[END_REF] [4] [START_REF] Li | Applications of Radar-based Nowcasting Techniques for Mesoscale Weather Forecasting in Hong Kong[END_REF] [6] [START_REF] Suzukii | Development of Strong Wind Nowcasting System[END_REF], not yet forecasting. Some believe that the most difficult part of forecasting natural disasters is producing the mathematical formulas of it [START_REF] Nasution | Forecasting Natural Disasters of Tornados using mHGN[END_REF].

So, it is a strong indication that other methods than mathematical formulas to forecast a tornado are required. A number of researchers [START_REF] Nasution | Forecasting Natural Disasters of Tornados using mHGN[END_REF] have developed an artificial intelligent technology to forecast an upcoming tornado. Although there is no definite mathematical functions that can be used to determine the condition of a tornado, windspeed, wind-direction, air-temperature, and air-pressure that constitute a tornado, are all caused by physical states [START_REF] Nasution | Towards Real Time Multidimensional Hierarchical Graph Neuron (mHGN)[END_REF]. It means that the time-series of several physical values of wind-speed, wind-direction, air-temperature, and air-pressure determine particular tornado condition. It can then be figured out that the upcoming of a tornado is generally produced by particular physical patterns.

The physical patterns that have been used in mHGN [START_REF] Nasution | Forecasting Natural Disasters of Tornados using mHGN[END_REF] were not yet ready for forecasting tornados, although mHGN is able to recognize incomplete alphabetical patterns [START_REF] Nasution | Towards Real Time Multidimensional Hierarchical Graph Neuron (mHGN)[END_REF]. There are two major issues why mHGN is not ready to forecast tornados. First, the physical values of weather data need to be represented using sophisticated scheme so, that false positive and true negative rates can be reduced significantly. Second, the occurrence of a tornado not at the location where the measuring sensors of weather for wind-speed, wind-direction, air-temperature, and air-pressure are located. Therefore, a sophisticated approached is required so that the patterns from previous occurrences of tornados can be used as the training pattern for future purposes.

In this paper, those two issues are elaborated in order to improve the accuracy of mHGN in forecasting tornados.

Tornado Recognition

Natural disasters have become common issues in the world. United Nation's UNISDR has been the primary organization that works tightly with disasters around the world. Since natural disasters have occurred in many countries, people across the world are hand in hand helping each other in facing natural disasters. This situation has helped researchers in gaining data from sensors scattered in the world. Researchers have started since long time ago investigating new approaches in forecasting natural disasters. As many parameters and conditions need to be considered, many issues related to forecasting natural disasters need to be investigated further.

The possibility of the occurrence of a natural disaster varies, but it is still there in most parts of the world. For instance, the tsunami that hit Indonesia in 2004 had never been experienced by Indonesians for hundreds of years. This situation applies for other natural disasters. Some countries have experienced natural disasters more than others [START_REF] Nasution | Forecasting Natural Disasters of Tornados using mHGN[END_REF]. This situation has been the main reason why a country such as the US has spent a lot of efforts to deal with natural disasters. When the circumstances of a tornado to come up in an area have reached, it is very likely that the area will suffer from a tornado. Most tornados occurred in the US have caused damages, losses, and costs. However, this does not mean that only the US must concern with the occurrence of tornados.

The randomness of the occurrence of a natural disaster is not only in terms of the location, but also in terms of the time and the severity. However, the location and the time (season) of tornados to occur is generally the same. Previous tornados occur between April and June, and the most countries that have been hit are those in the southeast area of the US. Although many researchers in opinion that the severity and the average magnitude of natural disasters have increased since the last decade. However, it is still not clear how severe future natural disasters might be. The impossibility of measuring, or predicting the severity of natural disasters, has been the major cause of the difficulties in anticipating their occurrences. Many other researchers have suggested that, one way to deal with the randomness of the occurrence of natural disasters is through a disaster forecaster.

Early warning system [START_REF] Sorensen | Hazard Warning Systems: Review of 20 Years of Progress[END_REF] and now-casting [START_REF] Cheng | A Nowcast Model for Tides and Tidal Currents in San Francisco Bay, California[END_REF] [10] [START_REF] Kryvasheyeu | Nowcasting Disaster Damage[END_REF] [5] [START_REF] Kirschbaum | Advances in landslide nowcasting: evaluation of a global and regional modeling approach[END_REF] [6] that have been investigated and developed are not yet able to help people avoiding and mitigating natural disaster. If the time frame of detecting a tornado is short, people will not be able to save themselves away from the tornado. For instance, the forecasting approach that they [START_REF] Ogasawara | Disaster Prevention and Response Support Solutions[END_REF] [7] [START_REF] Bellaire | Nowcast with A Forecast -Snow Cover Simulations on Slopes[END_REF] have attempted is able to forecast the disaster, but only within one hour time frame. Despite those efforts of researchers, Sorensen [START_REF] Sorensen | Hazard Warning Systems: Review of 20 Years of Progress[END_REF] argues that in terms of prediction and forecasting, still no radical breakthroughs have occurred in the past twenty years. Due to its complexities, most natural disaster researchers are working on technologies that are not focusing on the forecasting techniques. Rather, they are concerned with how natural disaster alerts can be disseminated to the public [START_REF] Sorensen | Hazard Warning Systems: Review of 20 Years of Progress[END_REF]. They suggest that most difficult part in facing a natural disaster is about how to handle people when a natural disaster occurs. Additionally, most common recommendation for an early warning system is "how to evacuate." In relation with natural disaster alerts, it is also important that special attention must be taken for those who have disabilities.

It seems to be that researchers have tried to find an appropriate approach for working on three areas: natural disaster forecaster, now-casters, or early warning systems. However, they [2] [13] [10] also still integrate their system with disaster management systems. Even Doong et al. [START_REF] Doong | Development of An Operational Coastal Flooding Early Warning System[END_REF] suggest that the success of a disaster mitigation concept lies in the quality of the disaster management. This shows that their approach alone is not yet adequate to handle natural disasters nor tornados. The potential reason to this case is the fact that a system for handling tornados requires very complicated mathematical analysis. Many parameters and values need to be considered and included in their calculation [START_REF] Bellaire | Nowcast with A Forecast -Snow Cover Simulations on Slopes[END_REF] [5] [START_REF] Cheng | A Nowcast Model for Tides and Tidal Currents in San Francisco Bay, California[END_REF], and it is time consuming [START_REF] Kryvasheyeu | Nowcasting Disaster Damage[END_REF], but the system must run fast [START_REF] Cheng | A Nowcast Model for Tides and Tidal Currents in San Francisco Bay, California[END_REF], that it can be used to warn people as early as possible.

As already mentioned, although the randomness of the occurrences of a natural disaster has caused difficulties in handling it, the development of every natural disaster still follows natural science characteristics and rules. Each part of a natural disaster of tornado owns specific location, time, patterns and characters. A tornado develops its twist due to hot and cold winds that move from the opposite directions. Not only the opposite winds play a role in developing a tornado, specific air pressure and air temperature are also significant contributors for a tornado's development. Yet, the difficulty to gain the measured values of those characteristics has become a new challenge in recognizing tornados before it turns up.

The steps that a tornado builds before its strong and winding wind can be regarded as a pattern. It means that the recorded data from previous tornado disasters plays a big role in recognizing it. Therefore, the data must be kept properly. The data is the important source of clues for researchers to analyze the pattern of a tornado. When patterns of tornados can be memorized, it is a strong possibility that when one of the patterns turns up, a system that can recognize patterns can be used to recognize a tornado early before it becomes a strong and destructive one. Such patterns are the most important part of mHGN for forecasting tornados hours before they strike. Unfortunately, the data provided by weather stations such as data of NOAA is not so accurate. The location of weather sensors is not exactly where the previous tornados occur. Before discussing the approach through the deployment of mHGN, it is still important to review the fundamental concept of mHGN.

Multidimensional Hierarchical Graph Neuron (mHGN)

The purpose of describing mHGN [START_REF] Nasution | Towards Real Time Multidimensional Hierarchical Graph Neuron (mHGN)[END_REF] in this paper again, is to provide an easy way for readers to review its concept and its principle.

The need for solving multidimensional problems has been discussed since a long time ago. People are aware of that to handle complex problems, values taken from numerous dimensions must be considered and calculated. Otherwise, the result that comes up after the calculation-analyzing just a few parameters-cannot be considered correct. In most cases, such a condition will produce very high false positive and true negative error rates. Another issue related to solving multidimensional problems is the method that will be implemented. In a complex system, not only the number of dimensions is large, but how all the dimensions are interrelated to each other, or independent on one another, is often not clear.

Natural disaster system is a suitable example as a multidimensional system. Therefore, forecasting natural disasters can also considered as solving a multidimensional problem. Not only the longitude and the latitude determine the condition of natural disasters, air-temperature, air-pressure, air-humidity, wind-direction, and wind-speed also play a big role in causing natural disasters of tornados. A problem that still exists is the interdependency amongst those tangible and intangible values (industrial development, people movement, etc.). It is so difficult to figure out a formula that constitutes such interdependency. This is, therefore, a strong indication that such multidimensional problems may be solved using artificial intelligent approaches such as mHGN.

Experiment Results

For the experiment, each neuron (GN) is operated by a thread. Various 2D-, 3D The experiment has worked on all the patterns of 26 alphabetical figures. Following the composition of the neurons, the alphabet patterns consist of 15X15 pixels. For the training purpose, the mHGN is first fed one-cycle with all the 26 non-distorted patterns. The order of the patterns during the training phase has been determined randomly. Then, to acquire the recognition results the mHGN is fed with a lot of randomly distorted patterns of alphabets. The recognizing accuracy is taken by calculating the average value of the results.

For the sake of the experiment, 20 distorted patterns for each alphabetical figure have been prepared. After acquiring the results, the experiment is repeated 10 times with the same steps, but each time the mHGN is trained with 26 patterns of alphabetical figures with randomly different order. So, for each alphabetical figure for particular percentage of distortion, in total 200 distorted patterns have been prepared as testing patterns.

There are 7 levels of distortion that have been tested, they are: 1.3%, 2.7%, 4.4%, 6.7%, 8.0%, 8.9%, and 10.7%. These levels have been so chosen based on the number of distorted pixels. The sizes of pixels represent the factor and the non-factor of the dimension of the patterns. By doing so, we can observe all the possibilities of distortion. So, in total there are 5200 (26 x 20 x 10) randomly distorted testing patterns. The following Figure 1 shows 5 samples of different orders of the training patterns: The following shows some results taken from testing 4.4% randomly distorted patterns, and the mHGN was previously stored with alphabetical figure patterns, and the order was IEFXMQYJHPDKTORZCUALBGVWNS. The value on the right side of each alphabet show the portion (percentage) of the pattern that is recognizable as the corresponding alphabet. The following shows 10 samples of distorted patterns of the alphabetical figure of "A" taken from the experiment of recognizing 5.8% randomly distorted patterns.

After collecting the results taken from testing 5200 patterns we can summarize how accurate the mHGN is, in recognizing different levels of distortion of 26 alphabets. The summary is taken based on the average accuracy values from all the steps. The following shows the summarized result taken from testing distorted patterns using five-dimensional 5X5X5X15X15 mHGN.

It can be seen from Figure 4 in the last column that the mHGN is able to recognize 91% of the 10.7% distorted patterns of 26 alphabetical figures. Some alphabetical fig- ures of A,C,E,G,I,J,L,O,S,T,U,V,X,Y,Z, are even 100% recognizable. Other patterns of alphabetical figures of H, K, M, N, are not very well recognized because they are visually and physically very similar. In fact, if this architecture is used to recognize different states of the same alphabet, such as regular-A, bold-A, and italic-A as the same alphabet, then mHGN will be able to gain better accuracy values. The following figure shows the differences of recognition accuracy amongst 15X15, 5X15X15, 5X5X15X15, and 5X5X5X15X15 mHGN architectures when recognizing 10.7% distorted patterns of alphabets.

1 E N W L I S P G H J D Y A X Q R C M F V O T U K Z B 2 R P J S O Q D V C K L E F G X Y A T Z B U W T H M N 3 G B H R Z C I Y X S J K D A N T Q V E W F U P O L M 4 L N I F R X B K O C T Z A Y G V U H P J Q S W E D M 5 C E T U N R H Y G D B K F M I X V S Q J Z W O A L P I A A 9 A 8 A 8 A
C Q Q 44 Q 15 Q 4 Q 15 Q 45 Q 15 Q 16 Q 13 Q 14 Q 13 Q 6 Q 14 Q 15 Q 15 Q 15 Q 45 Q 6 Q 44 Q 14 Q 45 20 U R R 11 R 12 R 12 R 5 R 12 R 11 R 38 R 5 R 6 R 10 R 10 R 7 R 37 R 10 R 37 R 37 R 38 R 11 R 2 R

Fig. 5. Differences of recognition accuracy amongst four different architectures

Time-Series in Pattern Recognition

Recognizing patterns of time series problem utilizes data that have previously been recorded regularly in timely manner [START_REF] Nasution | Realtime Weather Forecasting using Multidimenssional Hierarchical Graph Neuron (mHGN)[END_REF]. For instance, if the parameter that needs to be recorded is a single value, and the recording step is every six hours, then there will be 4 values recorded every day. In order to constructs the recorded values as a pattern, the data representation of the recorded values need to be developed so, that they can fit into a pattern recognition architecture. The following figure shows six ways of representing recorded data for 8 values of measurement. Definition: The distance between two values is the number of different bits between them. For instance, the distance between 001 and 110 is 3, whereas the distance between 00000001 and 10000000 is 2. Some people know it as Hamming Distance.

It can be seen from figure 6 that the data (value) is represented using binary numbers, and there are six possible data representations (Ver1 till Ver6). For the Ver1 (Binary Code Decimal), the distance between adjacent values varies. It is therefore not suitable for mHGN. For the Ver2 (Grey Code), the distance between two adjacent values is constant, which is one. However, the distance between Value 2 and 7 is also one. This is not suitable, as for mHGN the distance of 1 also means that the two values are very

Average

close to each other. In fact, value 2 and 7 are very different and very far from each other. Again, this is not suitable for mHGN. For the Ver3 and Ver4 (Ring Counter), the distance between any two values is constant, which is two. These are again not suitable for mHGN. For the Ver5 and Ver6 (Johnson Counter) the distance between adjacent values is constant, which is one. Additionally, the distance between any two values is linear with the value differences.

Fig. 6. Six examples of data representation for 8 levels of value

It seems to be that the Ver5 and Ver6 are the most suitable data representation that can be used with mHGN. However, such data representation will not efficiently utilize the binary combination. Using 3-bit data, only 3/8 or 0.375 is the occupation ratio. Using 4-bit data is the occupation ratio 4/16 or 0.25. The occupation ratio is 5/32 or 0.15625 when using 5-bit data. The facts show that the Ver5 and Ver6 data representation will produce less occupation ratio, the more bits is used. This is an indication that due to such an occupation ratio the pattern recognizer will have less recognition accuracy, the more bits is used. So, these are again not suitable for mHGN. The following is a better data representation.

In figure 7, there are three examples of 3-bit, 4-bit, and 5-bit data representation. It is shown that the distance between any adjacent levels in all samples is constant, which is one. Furthermore, between any two values which have value difference of two, the distance is also constant, which is two. Last, the distance between any two values which have value difference of three, the distance is again constant, which is three. For mHGN, such constant distances of 1, 2, and 3 are adequate to be used in tornado recognition architecture. Another characteristic of these data representations is that the representation is cyclic. It means that, if it is required the order of binary representation can be modified circularly without affecting the characteristic related to the distances. Using such better data representations, in all examples is the occupation ratio constantly 0.75. With such a constant occupation ratio the pattern recognizer will have constant recognition accuracy, regardless how many bits of data representation is used.

The figure 8 shows an example of recorded data taken from a single location measurement and each value has 3X5-bit values.

Value

00001 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 1 1 0 0 0 0 1 1 1 1 1 0 1 1 1 1 0 0 1 0 0 1 0 1 0 0 1 0 1 1 0 0 0 1 0 1 0 1 0 1 1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 0 0 1 0 1 1 1 0 0 1 1 1 0 1 1 1 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1 0 0 1

Time

It can be seen from figure 8 that the recorded values from parameter of 15-bit data construct a two-dimensional pattern of 10X15 architecture. Utilizing these recorded data, the pattern recognizer can forecast a tornado 6 hours earlier, when the same tornado occurs again. It means that if values have been recorded and the same pattern is recognized by the pattern recognizer, then the tornado is forecasted to occur again within 6-hour time with around 90% of accuracy.

Multidimensional Graph Neuron for Tornado Forecasting

In the previous section, time series value is described and represented so, that it can be forecasted through utilizing a pattern recognition, such as mHGN. In case of tornado forecasting, single parameter in a location, such as air-pressure, is not the only value that determine the occurrence of a tornado in the location within 6-hour time. Several other parameters, such as wind-speed, wind-direction, air-temperature, and air-humidity, play a big role in the occurrences as well. It means that the number of levels or a measured value will increase according to the number of parameters. In case 5 parameters need to be measured and each parameter contains 5-bit value, the required pattern structure would be 10X25. Also described in the previous section that measuring a parameter at particular point of location for several periods of time will generate a two dimensional pattern. If a series of points of the location need to be measured for several period of time, then the measured values will become a three dimensional pattern. The following figure depicts how some part of it will look like. Fig. 9. A row of data of 8-bit value build a 3D-Pattern Also described in the previous section that measuring parameters at particular point of location for several periods of time will generate a two dimensional pattern. If a series and linear of locations need to be measured for several periods of time, then the measured values will become a three dimensional pattern. If the location that need to be measured is an 2D area, then the measured values will generate a 4D pattern. Furthermore, if the location that need to be measured is a 3D area, then the measured values will generate a 5D pattern.

4.1

The Architecture of mHGN for Time-Series Tornado Data

The utilization of mHGN has introduced a new approach that a local tornado forecast can be operated using small and cheap components. The values of air-temperature, airhumidity, air-pressure, wind-speed, and wind-direction can be gained through ordinary sensors. The area that is covered by those sensors can be a 3D area, because such small sensors can be easily mounted in valleys or hills, or even vehicles. The sensors can be embedded in a tiny computer, such as Raspberry Pi. The tiny computer will be responsible to run several GNs. The values taken from the sensors will then be worked out within the GNs. The connectivity of neurons is developed within a tiny computer and through the interconnectivity of the tiny computers.

In short, to build a tornado forecast for particular location, five parameters need to be measured. They are: wind-speed, wind-direction, air-temperature, air-humidity, and air-pressure. So, if one parameter is represented through 5-bit binary data, then for the measurement of 5 parameters 25-bit data is needed. For the time series, 15 series of measurement will be carried out. For an area that needs to be protected by mHGN, 3X3X3 measurement points will be deployed. So, the mHGN dimension will be 3X3X3X25X15.

The positions of the 3X3X3 GNs will form a cylinder shape. In the cylinder, there will be three layers of circles. Each layer contains 9 GNs, in which 8 GNs will be on the border of the circle, and one GN will be located in the centre of the circle. The following figure shows the architecture of the positions of the sensors. The cylinder shape of the architecture has been chosen so, that mHGN still has an ability to recognize the same tornado pattern but developed with the direction different from the ones already stored. For the purpose of training, patterns from the previous tornados will be stored in the mHGN. Each pattern of a tornado will then be stored in mHGN eight times, following the number of eight major compass directions. It will look like as if the mHGN has stored 8 patterns of tornados. By having eight patterns for each tornado stored in mHGN, whenever the same characteristics of a tornado turn up but from different direction from the already stored ones, mHGN will be able to recognize it.

Data Handlings for Real Tornados

Two deadliest tornados occurred quite recently are the tornado that struck Joplin, Missouri on May 22, 2011 and the one in Hackleburg-Phil Campbell, Alabama on April 27, 2011. To store the circumstances, several parameters in these areas need to be stored in mHGN. Fortunately, the National Oceanic and Atmospheric Administration (NOAA) provides lots of data of: air-temperature, air-humidity, air-pressure, windspeed, wind-direction in most areas of the US. These data will be the major source for mHGN to store previous occurrences of tornados. In the case of Joplin, the following are several locations of stations that have recorded those data from their sensors including the map in the state of Missouri.

The following is the list of tornados scale F5/EF5 (the strongest) occurred In the US. The indicator F stands for Fujita and EF stands for extended Fujita. The scale has been named the same as the meteorologist Ted Fujita, who developed the scale.

The Bold Binary Values

The bold binary values in the sequence is used as the values of clusters. In an artificial intelligence approach, an intelligent system must first be trained so, that it will memorize patterns. Having such memory makes the system be capable of for instance recognizing previously trained patterns. The memorized values of those for-training patterns will be clustered into those bold values. In this research, it will be investigated, how many clusters are the best configuration that will achieve the most accurate results. Now, whenever a pattern needs to be recognized by the mHGN, all the values bold or not will be recognized as bold values. For instance, if the value taken from particular sensor is 10001 (index 19), the mHGN will recognize it as 10000 (index 20). The reason to this is because the smallest Hamming Distance of the value 10001 happens to be between this value and the value 10000, which is 1. Similarly, if the value taken from particular sensor is 10010 (index 21), the mHGN will recognize it as 10000 (index 20) as well. The reason to this is because the smallest Hamming Distance of the value 10010 happens to be between this value and the value 10000, which is 1. Someone might raise a question, whether there is a chance that the Hamming Distance 1 of the values 10001 and 10010 is not only compared to the bold value 10000, but also to one or two other bold values? To answer this question, the following Hamming Distance table shows the answer.

In the above table, it can be seen that in any bold row or in any bold column, the Hamming Distance of 1 is located in two places only. It means that the Hamming Distance of 1 belongs to those on the first adjacent neighbor values.

Conclusion

From the experiment results it is shown that mHGN has the capability to recognize multidimensional patterns. For simulating a tornado forecast, we have presented results of up to 5D architecture. As already discussed in [START_REF] Nasution | A Hierarchical Graph Neuron Scheme for Real-time Pattern Recognition[END_REF] and [START_REF] Nasution | Towards Real Time Multidimensional Hierarchical Graph Neuron (mHGN)[END_REF] there is no modification required if the architecture needs to be extended to bigger sizes of patterns. In the future this capability will be improved to the extent so, that multi oriented of multidimensional patterns will also be recognizable. At this stage it is also observed that mHGN still use a single cycle memorization and recall operation. The scheme still utilizes small response time that is insensitive to the increases in the number of stored patterns.

Fig. 1 .

 1 Fig. 1. Five different randomly ordered alphabets.

Fig. 2 .

 2 Fig. 2. The result of all the 26 alphabetical patterns that are twenty times 4.4% randomly distorted.

Fig. 3 .Fig. 4 .

 34 Fig. 3. Ten different randomly 5.8% distorted patterns of alphabetical figure of "A"

Fig. 7 .Fig. 8 .

 78 Fig. 7. Three examples of a better data representation for 3-, 4-, and 5-bit binary values

Fig. 10 .

 10 Fig. 10. The architecture of 3X3X3 sensors

Fig. 11 .Fig. 12 .Fig. 13 .

 111213 Fig. 11. The locations of F5/EF5 tornados

Fig. 14 .Fig. 15 .

 1415 Fig. 14. The Weather data of Philadelphia-Preston Station on April 27, 2011

001010 1 2 3 2 1 2 3 4 5 4 3 2 3 4 3 2 1 2 3 4

 4 00100 00110 01110 01111 01101 01001 01000 01010 11010 11011 11001 11101 11100 11110 10110 10111 10101 10001 10000 10010 00010 00011 00001 00101

  [START_REF] Cheng | A Nowcast Model for Tides and Tidal Currents in San Francisco Bay, California[END_REF]

	00.00	03.00	06.00	09.00	12.00	15.00	18.00	21.00	00.00	03.00

  The distances of bold binary values to other values

	Index	Value	Index	Value
	1	00101	13	11101
	2	00100	14	11100
	3	00110	15	11110
	4	01110	16	10110
	5	01111	17	10111
	6	01101	18	10101
	7	01001	19	10001
	8	01000	20	10000
	9	01010	21	10010
	10	11010	22	00010
	11	11011	23	00011
	12	11001	24	00001

In order to collect the suitable data that fit with the architecture of mHGN, the locations of the chosen weather stations that the data will be taken from, must build a figure like a circle, and the middle weather station must be located in the area in which a tornado has hit. The following is an excerpt of the data taken from a weather station Joplin in the State of Missouri.

Discussion

As is the case with pattern recognition of alphabets, patterns are more or less different to one another. However, in time series measurement data patterns, which are constructed from the measured values of the sensors, can be very similar to one another. Therefore, data representation of measured values before data is fed to the architecture of mHGN plays a big role in having very accurate results. False positive and true negative rate will also be indications to determine the quality of mHGN in forecasting natural disastesr.

The data that will be used to validate this work will be the data taken from different cities and different countries. As mHGN is trained one-cycle only, it is a challenge to choose which data is the right data for the training purpose, or the data is the consolidated data from a number of occurrences. When the appropriate training data has been applied, mHGN will then have a capability to forecast a tornado.

The main thing that needs to be developed for supporting the tornado forecasting technology is a sequence of binary values. Since the analog values taken from sensors (temperature, humidity, pressure, wind speed, and wind direction) need to be precise, so at least 5-bit binary values are required.

The requirements for the sequence of the 5-bit binary values are:

1. The Hamming Distance of any binary value to its first adjacent neighbour value is 1. 2. The Hamming Distance of any binary value to its second adjacent neighbour value is 2. 3. The Hamming Distance of any binary value to its third adjacent neighbour value is 3. 4. The sequence should be cyclic, which means that any binary-value can be either the first or the last member of the sequence.

The following are those mentioned binary values (Fig. 15).

It can be seen from the table above that all four requirements are fulfilled. To prove that the sequence is cyclic, it can be done through inserting the value at the index of 1 underneath the value at the index of 24. It can then be seen that all three requirements of the sequence (about Hamming Distances) are still fulfilled. So, after the index of 24, the sequence can be continued with the values of the index of 1, and 2, and 3, and so forth.


Laura Petersen
email: petersen@emsc-csem.org

Laure Fallou

Paul Reilly
email: p.j.reilly@sheffield.ac.uk

Elisa Serafinelli
email: e.serafinelli@sheffield.ac.uk

Public Expectations of Social Media Use by Critical Infrastructure Operators During Crises: Lessons Learned from France

Keywords: Social media, crisis communication, critical infrastructure operators

Previous research into the role of social media in crisis communication has tended to overlook how critical infrastructure (CI) operators might benefit from sites such as Facebook and Twitter, focusing instead on how emergency managers and the general public use such platforms. This paper sets out to address this gap by empirically exploring the expectations of French citizens in relation to the crisis communication strategies deployed by CI operators during major incidents. It does so by reviewing the literature on crisis communication, presenting the results of an online questionnaire, and comparing these results to the current practices of the A31 Highway in France, which were examined via a semi-structured interview. Results indicate that members of the public expect CI operators to communicate via traditional and social media. Therefore, the paper proposes that A31 Highway operators should expand their current practices to include social media.

Introduction

Crisis communication can be defined as "the provision of effective and efficient messages to relevant audiences during the course of a crisis process" [START_REF] Freberg | Using value modelling to evaluate social media messages: The case of Hurricane Irene[END_REF]. Social media has been identified as an increasingly important source of information during crisis situations [START_REF] Petersen | Public expectations of social media use by critical infrastructure operators in crisis communication[END_REF]. Previous research in this area has tended to focus on how emergency response personnel or citizens use social media during such incidents [3 -7], overlooking its benefits for other key stakeholders such as critical infrastructure (CI) operators. As such, there remains relatively little empirical research exploring public expectations of information provided by CI operators during crisis situations. The EU Horizon 2020 project IMPROVER (Improved risk evaluation and implementation of resilience con-cepts to critical infrastructure), makes use of Living Labs, or clustered regions of different types of infrastructure which provide specific services to a city or region. One such Living Lab is the A31 Highway in France. This paper will addresses these underresearched issues by presenting a succinct literature review on public expectations of disaster related information shared via social media. It then describes the French A31 Highway case study. After, the methodology of the online questionnaire and interviewbased study of the A31 French Living Lab are described. This is followed by a presentation of the questionnaire and interview results, accompanied by a comprehensive discussion on the subject and a conclusion.

General expectations of social media use in crisis

The public expect to be able to find information relating to disasters from both traditional and social media sources; recent research suggests that people use a combination of these sources to find information during disasters [6 -9]. For example, both Facebook and Twitter were heavily used by German citizens to find information during 2013 European Floods [START_REF] Kaufhold | The Self-Organization of Digital Volunteers across Social Media: The Case of the 2013 European Floods in Germany[END_REF]. One reason people turn towards social media is for its perceived efficacy in locating real-time information. During the 2011 Great East Japan Earthquake, a main reason that people turned to social media was the ease with which they could search for and obtain information [START_REF] Takeuchi | Utilization of Social Media in the East Japan Earthquake and Tsunami and its Effectiveness[END_REF]. Furthermore, a survey conducted in Europe by Reuter and Spielhofer (2016) found that 77% of respondents felt that information was made available faster during emergencies on social media than on traditional media [START_REF] Reuter | Towards social resilience: A quantitative and qualitative survey on citizens' perception of social media in emergencies in Europe[END_REF]. The same Reuter and Spielhofer survey found that 43% of respondents have already looked for crisis information on social media, with 70% of those having looked for road or traffic conditions [START_REF] Reuter | Towards social resilience: A quantitative and qualitative survey on citizens' perception of social media in emergencies in Europe[END_REF]. A study investigating Twitter data corresponding to 26 crises between 2012 and 2013 found that while on average 7% of tweets contained information related to infrastructure and utilities, these tweets were among the most retweeted during such incidents [START_REF] Olteanu | What to Expect When the Unexpected Happens: Social Media Communications Across Crises[END_REF]. Therefore it appears that information related to CI is of great interest to the public in times of crisis. There is also evidence that the public turn to the social media accounts of emergency responders during crisis situations and studies show that the public also expect responses from emergency services to their questions and comments posted on these platforms [3, 6 -7]. As previously stated, less is known about public expectations of social media use by CI operators. Clearly, citizens appear to expect updates from CI operators about service restoration [START_REF] Petersen | Public expectations of social media use by critical infrastructure operators in crisis communication[END_REF]. [START_REF] Reuter | Communication between Power Blackout and Mobile Network Overload[END_REF] examined public information needs from energy operators during a blackout and found that both the duration and cause were of key interest [START_REF] Reuter | Communication between Power Blackout and Mobile Network Overload[END_REF]. Some operators are already using social media to meet these information needs [START_REF] Petersen | Public expectations of social media use by critical infrastructure operators in crisis communication[END_REF].

Background on French A31 Highway case study

The French A31 Highway, also known by 'E25' in the European code, is an essential transport infrastructure network at a transnational scale. The section Nancy-Metz-Luxemburg is studied within the IMPROVER project. Its traffic is one of the most significant in France with 100,000 vehicles per day. The A31 Highway is used for intercity travel by the approximately 1,500,000 inhabitants of the Moselle valley and is also used by the numerous heavy goods vehicles from or to Luxemburg, Belgium, the Netherlands and Northern Germany. Significant traffic peaks can occur during holiday periods as it is used by Dutch, Belgian, German and French travelers. The traffic often reaches close to saturation conditions and traffic jams are observed daily from 15:00 to 20:00 in the section that connects Luxembourg to France and at the junction with A33 towards or from Metz. The studied infrastructure is managed by DIR Est (Direction Interdépartementale des Routes de l'Est -Eastern Interdepartmental Road Office), a decentralized service of French Ministry of Ecology which deals with the maintenance and the exploitation of public roads networks. In order to develop a policy regarding risk management, this service collaborates with the DDT57 (Direction départementales des Territoires de Moselle -Departmental Office of the Moselle Territories), another decentralized service of France, which deals with the sustainable development of territories, natural hazard prevention, implementation of regional planning politics and building permits. DIR Est also collaborates with other private roads exploitation companies (SANEF), regional authorities (DREAL), municipalities, dependent industries etc. in the event of a crisis. DIR Est and DDT57 are associated partners of the IMPROVER project. [START_REF] Ioannou | IMPROVER Deliverable 2.1 Methodology for identifying hazard scenarios to assess the resilience of critical infrastructure[END_REF] 4 Methodology

Research Questions

Specifically, two Research Questions emerged from the literature reviewed above:

1. What do French citizens expect of CI operators in regards to information provision during crisis situations? 2. How do these declared expectations compare to the current communication efforts of the French A31 Highway Living Lab? An online questionnaire and interview-based study was designed to investigate these questions. Ethics approval was sought and obtained from the respective authorities prior to data being collected.

Questionnaire

The target population for the questionnaire was French citizens aged 18 years and over.

In order to maximize the response rate, the questionnaire was translated into French and also made available in English. Convenience sampling was used for the questionnaire. It was structured as follows: First, a brief description of the project was provided and participants were informed of their right to withdraw from the project at any time, as well as how all data would be handled during the project. For the purposes of this questionnaire, respondents were presented with the following definition of a disaster: "an event which has catastrophic consequences and significantly affects the quality, quantity, or availability of the service provided by the infrastructure." Second, a Likert scale was used to measure participants' expectations. Participants were asked two questions regarding information provision. The first asked, "During and immediately after a disaster, I expect critical infrastructure operators to provide me with information…" and presented four scenarios: via calling their telephone number, on their website, on their social media site and through traditional media e.g. interviews with television networks or the radio, press releases. The second proposition asked, "During and immediately after a disaster, I expect critical infrastructure operators to respond to my questions and comments on their social media sites e.g. Twitter." The questionnaire also asked about the participants' demographics and social media habits. Data from the questionnaire was collected between 28 March 2016 and 30 April 2016. The questionnaires were translated back into English at the data entry stage. The questionnaire was disseminated through the IMPROVER consortium partners' contacts as well as through the Living Lab.

Sample characteristics

A total of 67 participants from France completed the online questionnaire. Due to the dissemination method, this self-selected sample was not broadly representative of the French population. Sample characteristics showed that 58% of the respondents were men, 40% were women, and one respondent chose not to answer that question. For comparison, in France 48% of the population are men and 52% are women [START_REF]Population totale par sexe et âge au 1er janvier 2017[END_REF]. Most respondents were highly educated with 88% of them having a university degree or higher, whereas only 33.5% of the French population have a university level education [START_REF]Adult education level (indicator)[END_REF]. Both young and old people appeared to be underrepresented in the study. Respondents aged 18-24 accounted for only 2% of the total sample (for comparison, French 18-24 year olds make up 8%), with 0.5% identifying themselves as aged 55 years and older (for France, 32% of the population is 65 years or older) [START_REF]Population totale par sexe et âge au 1er janvier 2017[END_REF]. Lastly, 91% of respondents have an account with a social media site such as Facebook or Twitter. For comparison, in France, 83% of households have Internet access and 85% of individuals have used Internet in the last three months [START_REF]Eurostat: Digital economy and society statistics -households and individuals[END_REF].

When asked an open-ended question to list up to three social media sites the respondent used most, 81% listed Facebook and 18% or less listed other popular platforms (Figure 1).

Interview

The findings were then compared to the current practices of the French A31 Highway. Two in-person, group interviews took place with actors from the Living Lab, one comprising of actors from DIR Est and another from DDT57. These included representatives from the office dealing with the intersection of the A31 between Nancy and Luxembourg, the office in charge of real time management for crisis interventions, the crisis management and alternative route designation office, and the urbanism and risk prevention office. Interviews were held in French and then data was translated into English for analysis. Data from the interview was collected on 25-26 May 2016.

5 Questionnaire results

Expectations for information to be provided on social media

When asked if respondents expected CI operators to provide disaster related information on social media, 79% strongly agreed or agreed (Figure 2). Few respondents (16%) were unsure or neutral in regards to the use of social media by CI operators to push disaster related information to the public.

Fig. 2.

Expectations that crisis information should be provided via social media

Expectations for information to be provided via other channels

CI operators were expected to use traditional broadcast media such as newspapers, radio or television to communicate with members of the public during such incidents, with 99% of respondents having strongly agreed or agreed with this statement (Figure 3). No respondents disagreed or strongly disagreed with this statement, and only 1% declared that they were unsure or neutral. The majority of respondents also had high expectations in relation to the availability of crisis information on the website of operators, with 94% having agreed or strongly agreed (Figure 4). Only 4% of respondents were unsure or neutral. When asked if respondents expected operators to provide information via calling a telephone number, 67% agreed or strongly agreed (Figure 5). 24% of respondents were unsure or neutral in regards to CI operators having a telephone hotline to make disaster related information available to the public.

Fig. 3.

Expectations that crisis information should be provided via traditional media Fig. 4. Expectations that crisis information should be provided via websites

Expectations for two way communication on social media

When asked if they expect CI operators to respond to comments sent by members of the public to their social media accounts, 61% of respondents agreed or strongly agreed (Figure 7). There was high uncertainty/neutrality (26%) in regards to responding to queries, and a large portion of respondents selected disagree and strongly disagree (13%). Lastly, 77% of respondents who expected CI operators to communicate via social media also expected them to respond to questions or comments.

Interview results

French A31 Highway operators identified two different types of general public who use their infrastructure: individual users and professional users (truck drivers). However their communication intended for users of the infrastructure is not dependent on which type of user they are addressing. The DIR Est is charged with communicating with the public. Since they are a public institution, they "must guarantee the reliability, usefulness, and credibility of information" before they share it with the public (Interviewee 1). DDT57 gather and synthesize data to be shared with the crisis cell and with the Préfet in times of crisis and do not communicate with the public. The emergency response structure in France is such that the Préfet is the one who makes all the crisis management decisions, including those related to communication. They find when it comes to their crisis communication, two elements are absolutely necessary to convey to the public: 1) delays associated with the crisis and 2) the cause of the crisis. Furthermore, the information must be updated regularly in accordance with which crisis management stage they are in and the ever evolving traffic conditions. According to our interviewees, "users demand to be informed of the situation" (Interviewee 1). They have learned from past mistakes where they did not inform the public of the situation, such as the example shared in this anecdote:

"In 2013, part of the highway was stuck in a standstill traffic jam. Some people began to turn around and go the wrong way on the motorway! This was happening around the same time that the highway was starting to circulate again, and so going the wrong way was risking a major accident. If the users had been provided the information related to the traffic jam, this crisis could have been prevented" (Interviewee 2).

A strategy focusing on communication with their users exists and is applicable in both normal times and crisis times. Crisis communication involves more frequent communication and requires a greater mobilization of actors than day-to-day communication. In general, they distinguish between two types of communication: programmed (when the event can be anticipated, such as certain weather related events or foreseen traffic peaks, for example during holiday periods) and real time (when the event cannot be anticipated). Programmed communication uses three main channels: traditional media (via the press), the DIR Est website, as well as collaborating to put the information on Bison Futé, the official French government website relating to traffic conditions. They may also implement warning levels when appropriate. For real time communication, they have a partnership with the radio station France Bleu, which allows them to not only disseminate traffic information at scheduled times, but also in the form of "flash" information, whereby the regular programming is interrupted in order to warn the listeners/highway users. "These information 'flashes' are sometimes picked up by other radio stations," said one operator (Interviewee 3), "but we do not have partnerships with other stations." Lastly, they communicate via VMS (variable messaging signs -electric traffic signs used on road ways to convey relevant information), which "allow us to communicate as closely as possible to 'in real time' when an incident occurs" (Interviewee 3). During crisis times, the press takes on an even greater role in information sharing. The DIR Est inform not only the radio but also television news channels and online news sites about sudden onset crisis events. They see their job as provider of critical, crisis-related information to the press, and the job of the press as messenger of said information to the public.

The radio and the VMS are seen by the operators to be the most effective means of communicating with the public. The VMS are especially effective as they are seen by the current users and can be implemented very close to a given incident. The radio station France Bleu is listened to by many of the highway users, and in the case of an incident, operators from DIR Est said that users already know to turn on that station to find out more information. Concerning the radio, however, DDT57 brought up a limitation. "The problem with the A31 is that there is no dedicated radio station and the promotion of the partnership with France Bleu does not have as great an impact as a dedicated radio station would" (Interviewee 4).

They do not send out SMS warnings, nor do they use social media, as they believe their current strategy has "already proved its effectiveness" (Interviewee 1). They are aware that users may communicate information via social media, but they see this kind of information as being the subjective opinion of a given user and as such don't view social media as a reliable information source. They did mention that Loractu and ITLF (Info Traffic Lorraine et Frontière), two local news sources, are active on social media and discuss traffic conditions. However, there is no official partnership with them.

Discussion

High expectations for information to be available on social and traditional media channels

Results indicate that members of the public expect CI operators to provide disaster related information via both traditional and social media. Overall, expectations were high for all four channels studied. There was no disagreement among respondents when it came to expecting disaster related information relevant to CI to be available through traditional means such as radio or television. This further reinforces the idea that even to the public, digital media such as websites or social media platforms is meant to compliment more traditional crisis communication methods and not replace them. CI operator run websites should then be used as another channel for information dissemination, as 94% of respondents expected it. Respondents also have high expectations for information to be available on social media (79%) and as such social media should be included in crisis communication plans as well. High expectations for information to be available on the Internet via social media and websites demonstrate high expectations for operators to be proactive in pushing information to citizens. This is furthered by the high uncertainty/neutrality (24%) for there to be a telephone hotline available, which would consist of the respondent being proactive in searching for the information. Furthermore, this substantiates the importance of understanding the technological culture of the local population when putting into place crisis communication plans [START_REF] Fallou | Culture and Risk management in Man-made and Natural Disasters[END_REF]. The majority of respondents expected operators to respond to queries, confirming the importance of two-way communication. However, the high amount of disagreement with this statement furthers the idea that pushing information to citizens should be more of a priority for operators than responding to queries. Respondents who expected CI operators to communicate via social media also had high expectations for a response. This can be explained by the fact that people tend to use the media platforms that they are already familiar with during crisis [19,[START_REF] Steelman | What information do people use, trust, and find useful during a disaster? Evidence from five large wildfires[END_REF]. High expectations for a response from operators further reflects the fact that social media generally encourages interaction and dialogue between users as it is an information space devoid of hierarchy [START_REF] Giroux | Using ICS & Social Media in Disasters: Opportunities & Risks for Government[END_REF].

7.2

Expectations for social media use are not currently being met by the operator

The current public communication efforts of the operators at the A31 Highway appear to meet public expectations when it comes to using traditional media and website channels. They currently have partnerships with various members of traditional media, from print media to radio and online news. The importance in using traditional media as a means for crisis communication is acknowledged by the operators. The A31 Highway also publishes crisis related information on their website. However, as they do not have social media accounts, they are not yet meeting this expectation. It is important however to keep in mind that this expectation may be an uninformed one as the IMPROVER questionnaire did not establish what the participants understood as crisis-related information on social media. Limitations with the use of social media include those laid out by the operators, namely reliability of information and the need to verify it. Despite this, the fact that respondents have high expectations for operators to use social media clearly indicates that this is an area where the operators can improve the effectiveness of their crisis communication. While the operators stated that they felt their crisis communication to already be efficient, respondents clearly demonstrate a desire for information to be shared on social media, indicating either that participants didn't understand the implications of using social media for crisis communication or a gap in perception of the crisis communication plan. As an institution of the French government, the A31 Highway operators must follow a hierarchical communication strategy, which is indeed the opposite of the information flows on social media. The A31 Highway appears to still be in a top-down approach, insisting on controlling information. The importance of providing verified information is also cited as a reason for not using social media. However, once verified, other institutions in the French government do communicate crisis information via social media (the Ministry of Interior for example). While the information in this case is indeed published less rapidly than that coming from ordinary users, it is still important to be able to find official information sources on social media. As the A31 Highway already provides verified, official messages in "near real time" on their VMS system, these messages could potentially be re-employed on social media. Indeed, multi-channel crisis communication is more effective [START_REF] Tierney | Disaster Response: Research Findings and Their Implications for Resilience Measures[END_REF][START_REF] Stephens | Organizational Communication in Emergencies: Using Multiple Channels and Sources to Combat Noise and Capture Attention[END_REF]. If the reason for not using social media is to avoid having to deal with queries that might be generated on such platforms, a transparent no response policy could help operators to meet citizens' expectations for information to be pushed via this channel, which were higher than expectations for queries to be answered, and continue with the current, top-down communication strategy.

Another solution to meeting social media expectations could be partnerships. As they already work in partnership with traditional media generally when it comes to public communication, it may be interesting to see if these actors' social media sites would be considered by the public as an appropriate source to find CI related disaster information. Perhaps an official partnership with either Loractu or ITLF could be envisioned, similar to the current partnership with Radio Bleu. As such, this approach could be another dimension of their existing relationships with news media. While the expectations studied here were for CI operators to provide the information directly, it may suffice to simply inform the public that crisis information related to the A31 Highway is available on social media at a partner's account in an official capacity. Further research should look into this. However, the comment on the limitations of using a partner radio station instead of a dedicated radio station during the interview also merits further investigation before putting into place more such partnerships, especially considering the results of the questionnaire, which asked about social media use by CI operators themselves.

The A31 Highway does not currently use information found on social media as they do not trust user provided information nor do they perceive it to be useful. Currently, social media is used by many different emergency response organisations to improve situational awareness. Indeed, social media monitoring leads to better situational awareness as it provides eye witnesses accounts. However, as expectations related to this were not asked of respondents, further research is needed to see if this is indeed an area that the public expect CI operators to further develop. In conclusion, it is suggested then that the operators expand their crisis communication strategy to include social media.

Limitations

The methods elaborated above have a number of limitations that should be acknowledged. As discussed earlier, the self-selecting sample engaged in the online questionnaire did not adequately represent the demographics of France. It should also be noted that the use of the website to distribute the questionnaire was likely to have skewed the sample in favor of those who used the Internet and social media on a regular basis.

Conclusions

Our findings suggest that CI operators should continue to use traditional media during crisis situations, and that this should be supplemented through the provision of disaster related information via the Internet, with websites and social media platforms maintained by CI operators. The French A31 Highway should expand their crisis communication efforts to include social media in order to meet public expectations. This may be difficult to put into place due to the current organisational culture which views social media poorly. Despite this, high expectations for information to be pushed via social media demonstrate the importance in including it in a multichannel crisis communication strategy. However, it should be acknowledged that this was a self-selecting sample that was not representative of the demographics in the population studied, and as it was an online questionnaire it most likely attracted people who generally use the Internet and social media. Further work is needed to explore the perspectives of citizens who are unable or unwilling to use digital media. A future questionnaire is being developed within the IMPROVER project to better understand these expectations. Lastly, the IMPROVER project is also currently working on the development of a communication strategy for CI operators to deploy during each stage of a crisis that will encompass both digital and traditional media platforms.

Fig. 1 .

 1 Fig. 1. Respondent's most frequently used social media sites (list up to three).

Fig. 5 .Fig. 6 .

 56 Fig. 5. Expectations that crisis information should be provided via calling a telephone number