50120130405031

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)
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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,
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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

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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 ).

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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
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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
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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.

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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.
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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
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(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
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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.
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