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  1. 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 5, September – October (2013), pp. 267-276 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET ©IAEME VULNERABILITY ASSESSMENT AND TRANSPORTATION MODELING IN KENDRAPADA POST CYCLONE DISASTER MITIGATION Bhoomi Gupta Assistant Professor, Maharaja Agrasen Institute of Technology, Delhi ABSTRACT This paper explores the coping strategies of the people of a coastal village in the wake of a cyclone. The vulnerability approach to disaster is adopted as theoretical framework of the research, in which disaster is considered as hazards affecting vulnerable people. Using structured data sheets of household heads, the coping strategies of a cyclone affected village community are examined. Transportation networks constitute one class of major civil infrastructure systems that is a critical backbone of modern society. This paper is aimed at developing a systematic approach for risk modeling and disaster management of transportation systems in the context of cyclone engineering. Keywords: Vulnerability, Disaster, Transportation Network. 1. INTRODUCTION Of the four stages of emergency management (i.e., mitigation, preparedness, response, and recovery), mitigation is the advance action taken to reduce or eliminate the long-term risk to human life and property from extreme events. In addition to the cyclonic mitigation measures that focus on transportation infrastructure, it is essential to understand and model travel demand in emergency situations when considering measures to secure traffic functions immediately after cyclone and restore the performance of the transportation systems Under emergency conditions such as damaging cyclones, traffic patterns differ significantly from “normal” traffic conditions due to the changes of post-cyclone travel demand and deteriorated network capacities. Estimation of travel demand is the first step in the traffic modeling but yet the part that has received the least attention. The most challenging obstacle to overcome, before deploying traffic modeling for planning applications, is to estimate and predict accurate origin-destination demand. The emergency traffic relies on the operational ability of the transportation infrastructure, and largely on the response of the evacuating public. Various factors influence public response, 267
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME including time of day and day of year, household location and structural characteristics, gender and age, disaster-specific threat factor, perception of risk, information source and type, provision of evacuation transportation assistance, local authority action, presence of children or disability in the household, etc. The manner in which these factors are addressed has direct effect on the pattern of travel demand. Post-cyclone change of traffic demand is partially related to the evacuation of residential and other critical facilities due to excessive cyclonic damage. 1.1 Study Areas The study area consists of 262 villages lying within 10 kilometres from the coastline in Kendrapada district of the state of Orissa. This region is the most cyclone prone region of India and the annual cyclone probability of this area is nearly 1, implying that it faces at least one cyclone (of different intensity) every year on an average. The distinct features of the proposed research are its introduction of the origin-destination (OD)-independent performance metrics and efficient optimization problem formulation, its accounting for post-cyclone travel demand changes, and its inclusion of assessment of reachability reliability of transportation systems [2]. The present paper tries to address these limitations and develops vulnerability indices due to cyclone and storm surge threats for villages lying within 10 km areas from the coastline in Kendrapada district of Orissa which is one of the most vulnerable districts of India. Compared to the previous studies where vulnerability indexes are defined as either the multiple or averages of the different threat parameters, we define these indices on the basis of the probability of witnessing non-zero human casualty due to very severe cyclones hitting these areas. We take into account hydrological, environmental, meteorological, infrastructural and socio-economic factors to define the vulnerability indices and present a disaggregated picture of the factors impacting vulnerability in diverse ways. We use cross section data on village-level human casualties witnessed in these areas during the super cyclone of Oct 1999, as also infrastructural and socio-economic data of the area for the same year to do our analysis[1]. Vulnerability to extreme events is usually addressed for macro units (districts or provinces) whereas the relative vulnerability of micro units may be more useful to a policy maker. The present paper addresses the vulnerability of coastal villages to cyclones and storm surge risks and identifies the physical and socio-economic factors strongly impacting the vulnerability of the villages. Rather than using a composite or aggregative index, we define the vulnerability index as the probability of facing nonzero deaths due to severe cyclones and calculate the indexes from a cyclone impact (human casualty) function using both Poisson specifications. 2. NETWORK BASED PERFORMANCE MODELING FRAMEWORK 2.1 Methodological Framework The methodology is divided into three specific stages: Stage 1: In this stage the vulnerability index is determined as the probability of facing non-zero human deaths due to cyclones and this probability will then help in calculating the overall vulnerability index as a function of human casualty function. This probability can be calculated manually by Poisson’s Distribution as the basic input to calculate the human loss and casualty function since it has a single parameter which is taken as the mean(assumed to be equal to variance) of the distribution. Since here in this distribution, the mean value is equal to the variance, then the test of inference is always found to be reliable. If mean is not equal to variance, the test of inference is highly unreliable. This also leads to the avoidance of over dispersion. The input variables in this 268
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME equation will be the factors leading to cyclones and adding factors of vulnerability. It is clearly understood that the Poisson’s distribution in this case as: Let the variable i stand for a particular village: Vi = Probability (Yi > 0) Where Yi indicates a non-negative, non zero value equal to non-zero human deaths. Vi = Cyclone Impact i (= human deaths i) = fn(Hazard i , exposure i, Adaptive Capacity i) Here we estimate the human casualties or the impact function; hence finding the vulnerability index Vi as probability of facing non zero deaths for various villages in the kendrapada district of Orissa with the help of the estimated data of the variables. This equation was the foremost in the studies carried out so far : Vi = F * T * P Where F= Cyclone Frequency T= Topography P= Population Density This resulted in giving a crude result of finding out the vulnerable coastal districts but had the following limitations as: a) Vulnerability is always calculated for macro units (i.e., districts) but should be addressed at the micro level) i.e., village level). b) The variables chosen are either multiplicative or average values; rather we should choose individual (independent as well as dependent variables). c) The distribution pattern of population can never be uniform (as taken in previous studies) and has to be a non-linear function. d) Many natural environmental factors like mangroves, vegetation cover have not be taken account in most of the studies. To address the above stated issues, Stage 1 work shall focus on the calculation of probability of non-zero human deaths in cyclone hazardous area by inputting the following sorted out list of variables:Vi = fn (Yi ) = P (Yi >0) = 1- P (Yi = 0) Decision Variables are: i. ii. iii. iv. v. vi. vii. Population At Risk Socio-Economic Variables Hydrological Variables Cyclonic Variables Environmental Variables. Variables Associated By Agencies (Government or Private) Infrastructural Variables. Dependent Variable= Yi = No. of Deaths in village i. So, Yi = fn.(Population i , Socio-economic factors i ,hydrological i , cyclonic_factors i , environmental i , agencies i , infrastructural i ). 269
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME These indicators or the variables can be categorized into various subsets like: i. a) b) c) d) e) f) Socio-economic variables: Scheduled Castes - % of scheduled caste people in village i. Literate - % of literate people in ith village. Cultivators - % of cultivators in ith village AgricultureLabour - % of agricultural labours in ith village Marginal Labour - % of marginal workers in ith village. Household workers - % of household workers (working in self-owned/household factories and industries) g) Other Workers - % of other workers in ith village (like engineer, teacher, doctor, priest) ii. Cyclonic Variables: a) Surge_level - Level of Sea elevation (in metres) at various coastal points. b) Distance_centre - Minimum Distance Of a village from the centre of eye of cyclone or from the cyclone path. c) Distance_coast – Minimum distance of village i from the coast. iii. Hydrological Variables: a) Distance_minriver - Minimum distance of a village i from a minor river. b) Distance_majorriver – Minimum distance of a village i from a major river. iv. Infrastructural Variables: a) Distance_mainroad - Minimum distance of village i from the main /metallic road. b) Distance_villroad – Minimum distance of village i from the village road (if present) v) Natural environment_vegetation variables: a) Mang_width- Width of the historical mangrove cover between the village and the coast. b) Mangrove_width_new – width of the new and existing mangrove cover between the village and the coast. c) Casuarine_treecover- 1 for presence and 0 for absence of casuarine tree cover in the coastal village. If the data (statistical) for all the 262 villages be taken up for the Kendrapada district of Orissa; or if not entirely possible; for the 68 closer to coastal line villages; then for each village vi (for i=1 to 68) the probability of non-zero human loss or death due to each of the above stated indicator be calculated individually ;hence calculating or summing up the entire vulnerability index for each village. => Yi = Probability (contributing to human loss) due to each of the above stated indicators i.e., Yi = fn (Surge_level, Distance_centre, Distance_coast, Mangrove_width_new, Casuarine_treecover, Distance_villroad, Mang_width…for all indicators). Thus, it will reflect a clear indication on the effect of each individual variable or the Yi. By such a calculation, we can find out the variables which increase death more significantly than the others. e.g., we can easily find out how the villages located in mangrove inhabitance are more prone to cyclone destruction than the others and what is the relative percentage of its effect. The work reflects the vulnerability of specific villages due to a specific indicators. These indicators shall help 270
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME in giving a clue and a clear indication on the usage of specific ICT tools to limit and discourage the values of specific vulnerability indicators as indicated. Stage 2: The second phase of the work emphasizes on the usage of the selected variables which effect more prominently and cause more vulnerability and then to apply solutions to it (directly or indirectly associated with ICT), e.g., if we identify the contribution of dist_minriver or dist_major river to the cyclone devastation and apply ICT tool like GIS map on these villages which have higher probability; thus selecting out those places where cyclone shelters (alternate) can be placed; smooth evacuation be made with the help of GIS map previews. To analyze the scenarios, the summary table can be constructed as: Most Vulnerable Indicator (1) Lesser Vulnerable indicator (2) - - - Least Vulnerable indicator (n) Village 1 Village 2 Village 3 Village 4 Village261 Village262 Table 1: Table indicating the vulnerability indicators for the respective villages Vulnerability probability due to each factor: Low Medium High Range 1 Range2 Range 3 Each probability calculated for each factor can be categorized into 3 levels (Low, Medium, High). For example, Surge_level probability is from (say) 0.03 to 0.95 then we can divide this range of 0.03- 0.95 into 3 levels – low (0 - 0.33), medium (0.34 - 0.66) and high (0.671.00) and then proceed:- Fig 1. ICT tool selection based on vulnerability assessment 271
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME The cost or the benefit values clearly indicates the effect (positive/negative) of a vulnerability factor j on village i (for a particular cyclone) and usage of ICT Tool k, hence giving a resultant value. This value indicates the chance/ probability of ICT tool to be applicable on that village i, hence giving a clue to effective evacuation or better hazard mitigation. Thus, an analysis evacuating lives saved = economy saved can be calculated. Stage 3: Evacuation Planning The work as analysed in the previous stages tried to develop an influence diagram or a Bayesian Network for modeling risk and hence has developed ICT implementation on the vulnerability indicated areas. These ICT tools implemented helps in minimizing the risk of loss and provides benefit in the form of evacuation planning. To map evacuation routes on the transport (road) networks of the identified villages of kendrapada for massive evacuation using optimization of the carrying capacity of the roads and the maximization of the capacity handling at the end/destination points. After identification of the village i to be evacuated (after ICT alert has been provided effectively due to previous stage when we know that vulnerability indicator j of a cyclone makes that village i more vulnerable and hence needs to be evacuated on an urgent basis.); then a methodology is developed to identify the topological network of the routes/roads of village i based on the demographic features of that village. This methodology then results in the computation of an optimization model that can be used to identify and evacuate to the safer places (say the cyclone shelters). This model integrated with a GIS System can be used to make reference (ready real time scenario based) maps (road wise) for the evacuation risk minimization. Evacuation From the village i to village (shelter) depends on a crude number of factors: • • • • Number of people in need or in demand of the evacuation i.e., demand_pop_evac (popvi) Transport Carrying (vehicle load) for population popvi for village i to village j., i.e., Trans(vij) Rate at which the demand is fulfilled(RD) Rate at which the capacity is actually provided(for shelters),i.e., Rcap Finally, Rcap - RD = People at Loss ( i.e., Human Loss) e.g., During a cyclone, an area needs to be evacuated, even though the bulk capacity of all vehicles may be large enough to carry out all the people in danger out of that area, the maximum flow rate of the transport or road network (number of lanes available) may become a limiting factor. e.g., there may be no information about the shortest paths in the topological graph of the road network, e.g., or a shortest path ( Pi ) may be practically feasible to be used because of some obstructions etc. For this reason, emergency planning zones (EPZ)[3] also are sketched well in advance which help in evacuation planning. Thus the basic procedure is a two step process:Step1: Build up a topological (road based) network from the GIS map of the district available. Sketch a graph (directed) out of this network. Step 2: Build up nodes and edges of that graph in such a way that the nodes are assigned as villages and edges represent the total traveling distance on these roads. Now we find the shortest path/optimal path on that graph from node i to node j and also create a subset of shorter paths on every node or intersecting points. 272
  7. 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME To calculate the Evacuation Risk Factor: Step 1: The transport capacity is calculated as whether the total vehicles available can carry the total population in demand. Step 2: Ratio depicting the clearance time of people can be calculated as: TCL(vi ) = ( popvi * (1/ppv (vij )) ) / capl(vij ) where TCL(vi ) = The clearance time of people from village i to its neighbourhood. popvi = Population of village i and its immediate neighbourhood which is calculated as: = (Number of houses) * (Number of people / house). ppv (vij ) = People per vehicle during evacuation from village i to village j. capl(vij ) = capacity of lanes/roads available for evacuating people in the units of vehicle per minute. Hence TCL(vi ) is calculated in units of minutes. Let us take an example of a sample routing map from village i to village j in its simplest model. Case 1: For a single lane possible for exit:For popvi =2000 houses, Number of persons/house=3(average) and for 2 persons/vehicle exit. Thus, for a single lane:- - Households (say a total of 2000 in a village i ) Number of vehicles required = (2000*3* (1/2))/1= 3000 vehicles. which indicates that more lanes need to be added simultaneously. Case 2: For a double lane possible for exit:Case 2: Number of Lanes: 2 Total Vehicles required =(x number of houses * y number of persons /house * z number of vehicles/ persons for exit)/Number of Lanes = (2000*3* (1/2))/2 =1500 vehicles. 273
  8. 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME Case 3: For simultaneous 4 lanes: Total Vehicles required = (2000*3* (1/2))/4 = 750 vehicles. Fig 2: Study area of kendrapara and its transportation network. Constraints in the specified model: The above calculated TCL(vi ) value does not take into account the factors like accidents (on the intermediary roads), congestion models, human behaviour etc. These time delays need to be added to the TCL value to get the effective clearance time. Thus, this value is a lower bound value estimate on the actual time to clear the village i. Analyzing the TCL value, it is found that low the value of TCL, it is easier to evacuate the area in a speedy manner; higher the value of TCL then it might cause a difficulty in the evacuation process; especially if the rate of incurrence of cyclone is higher than the rate of evacuation on that shortest path found. Now we calculate the exit capacity in terms of exit lanes/routes available. Then TCL is evaluated in terms of bulk demand per lane(bdl),i.e., This factor is actually bdl. Thus an extrapolated value can be deduced which can indicate the possibility of building up a digraph .The designated set of nodes for the digraph analysed are as follows: Vi = {V1, V2, V3, V4, V5} Vj ={V7,V8,V9,V10,V11,V2,V3,V4} VK = {V5, V6, V11, V12, V13, V14, V15} Thus from evacuating from village i to village k, we need to find all possible shortest paths and its all possible subset of shorter paths from an optimal algorithm/integer programming model (maximization problem) so that at each intersecting lane, we have the set of next lanes to move on to reach destination cyclone shelter (V14) of village k, i.e., Source Node (Svi ) to destination Node 274
  9. 9. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME (Dvk ).Thus, two possible set of results are required for an effective evacuation on roads/transportation network:- Minimizing TCL (Clearing Time) by increasing the number of lanes for evacuation. Maximizing the shortest paths available by optimal path calculations integrated with real time GIS Map simulations and generations. Thus, mathematically, we can analyze:Maximize Z (Objective Function) = ( ∑ (ai *xi) / ∑ ∑ (cij * yij) ) For a given directed Graph G which is actually a GIS based topological map depicting all possible routes/roads for evacuation and with capacities on edges and population on nodes. Where ai = population at node i xi = { 1 if the node i is in the selecterd region r in village under focus;0 otherwise } yij = { 1 if the node i is in Vr and j is not in Vr ;0 otherwise} cij = number of lanes available between the area/region/segment ij. and xi <=S (where S= maximum size of Vr ) where Vr is the selected region (of digraph)for a particular source village under focus to be evacuated. 2.2 Technical Consideration To the specified Problem: 1. Changes in weather conditions during evacuation process. 2. Human Behavior. 3. Sudden Destructions of transportation infrastructure. 4. Change in population density in day and night time. 5. Differing age groups of people in need. These set of problems deal with routing from a specific set of source nodes to a set of destination nodes through a transportation network including the potential obstacles like congestion models, blocking probability, time delays etc.[5] Denoting the symbols used in the equation for minimizing Z, where Z=linear combination of 2 factors (Dismin and Tcl min). i.e., Z=fn (linear) { Dismin ,Tcl min} for k=index for kth shortest paths; if k= 1 (1st shortest path selected) Dismin = ∑ popvi ∑ (xij1 * dij1) ; where Dismin = ∑ popvi ∑ (xijk * dijk) The evacuee will travel Dismin only if he/she is assigned the first shortest route, therefore this Dismin is thw lower bound value for the evacuation paths set. Similarly, Tcl (min) =∑ t1(1) ∑ popvi ∑ (xij1 * αij1) Where αij1 =1 if the road link l is on the route present; 0 otherwise. and t1(1) = minimum expected travel time on the route selected. 3. STATISTICS AND RESULTS Poisson Coefficients of the human death regression in Orissa (n=262 villages lying within 10 km from coast in Kendrapada district) (by indicating basic vulnerability factors 275
  10. 10. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME Variable Dcoast Mangrove Mhabitat Topodumy Casurinadumy Dmajriver Dminriver Roadumy Population Literate Schedulcaste Cultivator Aglabor Margworker Outworker Poisson Coefficient 0.16 -1.11 -0.22 1.77 -0.42 0.245 0.04 0.43 0.005 -1.65 1.006 0.54 0.29 9.57 3.6 3. CONCLUSION The OD-independent performance metric of network flow capacity is employed to assess the system performance of transportation networks—the performance of transportation systems with damaged roads are calculated by solving the maximum-flow problem in the simulated cyclone scenarios. Moreover, based on physical damage of network components, the network reachability of transportation systems can be evaluated by the system connectivity reliability. REFERENCES 1. Chittibabu, P. et al. “Mitigation of flooding and cyclone hazard in Orissa, India”. Natural Hazards 31 , 455-85 (2004). 2. Das, Saudamini, “Storm Protection Values of Mangroves in Coastal Orissa”, in P. Kumar, Sudhakara Reddy Ecology and Human Well-Being, New Delhi, Sage Publications, (2007). 3. Hughes, P. and G. B. Brundrit, “An index to asses South Africa’s vuilnerability to sea level rise”, South African Journal of Science, 88: 301-311 (1992). 4. http://www.imd.ernet.in/section/nhac/static/cyclone-history-bb.htm 5. http://www.oed.comSharma, U. and A. Patwardhan, “Methodology for identifying vulnerable hotspots to tropical cyclone hazards in India”, Mitigation and Adaptation Strategies for Global Change, DOI 10.1007/s11027-007-9123-4 (2007). 6. Nadia Khelif, Imed Ben Slimène and M.Moncef Chalbaoui, “Intrinsic Vulnerability Analysis to Nitrate Contamination: Implications from Recharge in Fate and Transport in Shallow Groundwater (Case of Moulares-Redayef Mining Basin)”, International Journal of Civil Engineering & Technology (IJCIET), Volume 3, Issue 2, 2012, pp. 465 - 476, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316. 7. Mohd Umar Farooq and Dr .Khaleel Ur Rahman Khan, “The Novel Techniques for Data Dissemination in Vehicular Networks to Triumph Over Broadcast Storm Problem”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 3, 2013, pp. 264 - 272, ISSN Print: 0976-6480, ISSN Online: 0976-6499. 276