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Complex Risk Analysis of Natural Hazards Through Fuzzy Logic

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Complex Risk Analysis of Natural Hazards Through Fuzzy Logic Journal of Advanced Management Science Vol. 1, No. 4, December 2013 Complex Risk Analysis of Natural Hazards through Fuzzy Logic P. Zlateva ISER, Bulgarian Academy of Sciences, Sofia 1113, Bulgaria Email: [email protected] D. Velev University of National and World Economy, Sofia 1700, Bulgaria Email: [email protected] Abstract—The paper presents a fuzzy logic approach to natural hazard for given monitoring region. This approach complex risk analysis in regard to each of the natural hazards is based on the available statistical data and the expert for a given monitoring region. This approach is based on the knowledge. available statistical data and the expert knowledge. The The proposed risk analysis is envisaged to be calculations of the complex risk are done for five regions in implemented as a part of a Web information system for risk SW Bulgaria (Dupnitsa, Blagoevgrad, Simitli, Kresna and Sandanski). The proposed risk analysis is envisaged to be management of natural disasters. This system can be implemented as a part of a Web information system for risk successfully used in e-government [6]. management of natural disasters. II. AN APPROACH TO COMPLEX RISK ANALYSIS OF Index Terms—risk analysis, fuzzy logic, natural hazards, NATURAL HAZARDS THROUGH FUZZY LOGIC Europe, SW Bulgaria The idea is the approach to complex risk analysis to take into account quantitative and qualitative characteristics of all natural hazards in monitoring region. I. INTRODUCTION The approach is designed on basis of fuzzy logic and Recently the negative impact of natural hazards on includes the following steps: human life, economy and environment is increased [1]. Step 1: The basic sets and subsets for risk level of Statistic data and scientific research show a growth in given region and severities of natural hazard are number and severity of natural disasters compared to introduced and they are described in natural previous years [2]. language: The annual losses resulting from floods, hurricanes, droughts, earthquakes, tornadoes, etc. cost billions of 1) Complete set of risk level of monitoring region R dollars. Despite the tremendous progress in science and is divided into five subsets of the form: technology, the natural hazards considerably affect to the R1 – subset “Very low level of regional risk”; socioeconomic conditions of all regions of the globe. R2 - subset “Low level of regional risk”; The natural hazards are impossible to avoid, and R3 - subset “Middle level of regional risk”; infrastructure elements and communities cannot be made R4 - subset “High level of regional risk”; totally invulnerable. The only viable solution is the R5 - subset “Very high level of regional risk”. complex risk analysis and subsequent development of 2) Complete set of severity of natural hazard H is combination of mitigation and adaptation strategies [3]. divided into five subsets of the form: There are many qualitative and quantitative methods for the risk analysis. However, it is necessary to point out, that VS – subset “Very small severity of natural the complex risk analysis from the natural hazards is done hazard”; under the subjective and uncertain conditions. The fuzzy S - subset “Small severity of natural hazard”; logic is an appropriate tool for risk analysis. This method M - subset “Middle severity of natural hazard”; provides adequate processing the expert knowledge and B - subset “Big severity of natural hazard”; uncertain quantitative data [4], [5]. VB - subset “Very big severity of natural hazard”. The purpose of the paper is to propose a fuzzy logic Here and below it is assumed that the all elements of set approach to complex risk analysis in regard to each of the R and D accept values in the interval [0, 10]. Step 2: The natural hazards (risk indicators) Manuscript received July 25, 2013; revised September 30, 2013. H={Hi}, i 1,...,n , which are typical for monitored This work was supported in part by the University of National and regions, are determined. World Economy, Sofia, Bulgaria under Grant NI 1-8/2011. ©2013 Engineering and Technology Publishing 395 doi: 10.12720/joams.1.4.395-400 Journal of Advanced Management Science Vol. 1, No. 4, December 2013 Step 3: The corresponding degree of importance in The results are presented in tables for each of the natural the risk analysis i is assigned to each natural hazard, as shown in Table II. hazard Hi. In order to appreciate this degree, it is TABLE I. RISK LEVEL CLASSIFICATION OF MONITORING REGION necessary to arrange all the hazards in decreasing importance so as to satisfy the rule Risk value Classification of Membership function of interval, r the risk level, Ri the risk level, n i 0 r 1.5 R1 1 1 2 ... n 0 and i 1 (1) i1 1.5 < r < 2.5 R1 1 = 2.5 - r R2 1- = If all indicators are equal importance, then 1 2 2.5 r 3.5 R2 1 1 3.5 < r < 4.5 R2 2 = 4.5 - r i , i 1,...,n (2) n R3 1- 2 = 3 4.5 r 5.5 R3 1 Step 4: A classification of the current value r of the level of regional risk as a criterion to split the set R 5.5< r < 6.5 R3 3 = 6.5 - r into fuzzy subsets is constructed (see Table I). R4 1- 3 = 4 6.5 r 7.5 R4 1 Step 5: The membership function “severity of 7.5 < r < 8.5 R4 = 8.5 - r natural hazard” for each value of hazard variable H 4 is calculated R5 1- 4 = 5 8.5 r 10 R5 1 Each hazard variable H i , i 1,...,n has a corresponding membership function , j 1,...,5 to the Step 6: The value r of the “level of regional risk" in ij regard to all the considered natural hazards for each five fuzzy subsets. of the monitoring regions is calculated The membership functions ij are defined with the The value rk of the “level of regional risk" in regard to following formulae: all the considered natural hazards Hi, i 1,...,n for each of 1, 0 Hi 1.5 the monitoring regions Xk, k 1,...,m are determined as μi1 2.5 - Hi , 1.5 Hi 2.5 follows 0, 2.5 H 10 i 5 n 5 5 r k qk , q (4) 0, 0 Hi 1.5 k j i ij j j j i ij j1 i1 j1 i1 Hi 1.5, 1.5 Hi 2.5 μi2 1, 2.5 Hi 3.5 A node point vector 1,2,3,4,5, is 4.5- H , 3.5 H 4.5 i i introduced. In this investigation the node point vector has 0, 4.5 H 10 i following elements 1,3,5,7,9 . 0, 0 Hi 3.5 Hi 3.5, 3.5 Hi 4.5 Hi TABLE II. MEMBERSHIP FUNCTIONS OF FOR MONITORING μi3 1, 4.5 Hi 5.5 REGIONS 6.5- Hi , 5.5 Hi 6.5 Monitorin Membership functions of Hi 0, 6.5 H 10 No i g region, X VS S M B VB 0, 0 H 5.5 1 1 1 1 1 i 1. X i1 i2 i3 i4 i5 1 Hi 5.5, 5.5 Hi 6.5 2. … k k k k k μi4 1, 6.5 Hi 7.5 3. Xk i1 i2 i3 i4 i5 8.5- H , 7.5 H 8.5 i i 4. … 5. m m m m m 0, 8.5 Hi 10 Xm i1 i2 i3 i4 i5 0, 0 H 7.5 i Step 7: The linguistic classification of the risk level μi5 Hi 7.5, 7.5 Hi 8.5 of monitoring regions in regard to all the considered 1, 8.5 Hi 10 natural hazards is carried out. (3) The calculated value r of the variable “level of regional It are carried out the calculation of the values of the five risk" is classifies on the basis of the data in Table I. k membership functions “severity of natural hazard” ij in The main result of the classification is linguistic description of the risk level of monitoring region Ri in regard to each of the natural hazard Hi, i 1,...,n regard to all the considered natural hazards. Additional for each of the monitoring regions X , k 1,...,m . k result is the degree of expert certainty in the correctness of ©2013 Engineering and Technology Publishing 396 Journal of Advanced Management Science Vol. 1, No. 4, December 2013 the classification which is given by value of corresponding between 120C to 140C from north to south for the studied membership function i. area [8]. Thus the conclusion about “level of regional risk” The rainfalls are relatively low 500-650 mm and are acquires not only linguistic form, but also characterization unevenly distributed. They are rare, but intense with for the reliability of this assertion. overflowing character. These rainfalls in combination with easily-disintegrated rock cause the intense erosion, III. A COMPLEX RISK ANALYSIS OF NATURAL HAZARDS mud-rock flows and floods. FOR DIFFERENT REGIONS IN SOUTHWESTERN High summer temperatures, which frequency over the BULGARIA last decade increases, are a serious danger for the population. Throughout in the flat part of the Struma valley Bulgaria is located on the Balkan Peninsula, 0 Southeastern Europe (see Fig. 1). It is exposed to natural the annual maximum temperatures reach 38-40 C. In very hot summers of 2000, 2006, 2007 and 2009 the absolute hazards, such as earthquakes, floods, landslides, debris 0 flows, forest fires, hail storms, rock falls, snow avalanches, values over 40 C are reported, for example in Sandanski - 44.60C (2007).
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