SECED 2015 Conference: Earthquake Risk and Engineering towards a Resilient World
9-10 July 2015, Cambridge UK
AN ASSESSMENT OF EARTHQUAKE VULNERABILITIES IN
KATHMANDU, NEPAL FOR IDENTIFICATION OF OPTIMAL
IMMEDIATE AID SITES
Andrew MACLACHLAN1, Eloise BIGGS2 and John BEVINGTON3
Abstract: Pre-event vulnerability assessments are an emerging discipline within earthquake
risk studies. However, owing to extensive data collection for appropriate building stock
representation and associated vulnerability, the majority of studies fail to comprehend the
multifaceted nature of building vulnerability for pre-event assessments. Furthermore, few
studies explore optimal immediate aid sites for the distribution of aid materials in a post-event
scenario. New and novel tools recently released by the Global Earthquake Model (GEM) are
implemented to overcome limitations of previous studies, permitting standardised repeatable
Worldwide results, fulfilling the call from the Organisation for Economic Cooperation and
Development (OCED) for the establishment of open source risk assessment tools.
Introduction
In the last decade more than 200,000 people lost their lives due to ramifications associated
with earthquakes, with a global annual average loss of $18.65 billion in economic damage
observed between 2000 and 2009 (Jaiswal et al., 2011; Guha-Sapir et al., 2011). In Nepal,
over 11,000 people died from earthquake consequences during the 20th Century. Evidently
earthquakes present a significant hazard to society not only in terms of human fatalities but
also economic cost. Consequently, pre-earthquake vulnerability assessments that are capable
of informing policy for future city development are critical for reducing risk (Geiß and
Taubenböck, 2013).
Kathmandu Valley exhibits a multifaceted issue regarding earthquake vulnerability. The Indian
plate is subducting under the Eurasian plate, with no significant earthquake having occurred
in the Himalaya during the last three centuries, presenting a significant earthquake risk (Bilham
et al. 2001; Gupta and Gahalaut, 2014). The Valley has a vast population of 2.5 million with a
high annual growth rate of 5-7% (Roberts, 2013). Additional characteristics include: poor urban
planning, with low compliance of building codes; and underlying sediment being mostly
composed of lacustrine materials and the Valley being surrounded by four mountains, which
may trap and redistribute seismic energy. Subsequently the cumulative effect of such
characteristics presents a catastrophic scenario in the event of a major earthquake.
Pre-event vulnerability assessments have the power to significantly reduce casualties and
mortalities if used in the appropriate manner. They require a form of exposure dataset
(commonly buildings), associated vulnerability (being the predicted effects of shaking on the
exposure dataset) and seismic hazard (Pittore and Wieland, 2013). A large portion of academic
literature only considers physical vulnerability in terms of building exposure. In this regard
structural analysis is undertaken whereby the structural capacity (capacity load) to sustain
seismic loads is compared to the earthquake demand, termed ‘capacity curve’ (Nastev, 2013).
The capacity curve is generally derived from the yield capacity point of the building; being the
force that exceeds the buildings resistance. A building will only remain standing if the ultimate
capacity point is not exceeded; the point at which the building can no longer withstand the
force. Subsequently, the building moves from elastic to plastic deformation (Malladi, 2012).
Following this, building-specific vulnerability functions (or models) identify the relationship
1
Mr, University of Southampton, Southampton, [email protected]
Dr, University of Southampton, Southampton, [email protected]
3 Dr, ImageCat Ltd., London, [email protected]
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between seismic intensity and damage to structures (defined in the exposure model), providing
the probability of fraction of loss with associated ground shaking, derived from fragility curves
(models of functions) indicating the probability of occurrence per damage state in relation to
the hazard and capacity curve (Chaulagain et al., 2015; Silva, et al., 2013; Thapaliya, 2006).
However, the majority of countries, in particular developing countries do not possess
appropriate building stock data. Consequently, alternative methods are sought in order to
generate building stock datasets. In this regard satellite remote sensing plays an integral part
in building stock generation. Nevertheless, earthquake prone developing countries are often
precluded from undertaking analysis due to the lack of country specific vulnerability functions
and earthquake models. The Global Earthquake Model (GEM) aims to heighten public
understanding for effective decision-making in earthquake scenarios. Consequently the GEM
constitutes a free toolset, including: the Inventory Data Capture Tools (IDCT)4 (Hu et al., 2014),
the Spatial Inventory Data Developer (SIDD) (Hu et al., 2014; Porter et al., 2014) and
OpenQuake (Crowley et al., 2014) to provide a transparent, repeatable and straight forward
methodology for utilising remote sensing with optimal ground data collection. The methodology
permits a systematic and standardised data flow for: field data collection, mapping scheme
review (a zonal statistically inferred building stock distribution), exposure dataset generation
and subsequent use within a Global earthquake model loaded with associated vulnerability
data. Geiß and Taubenböck (2013) and Mück et al., (2013) indicated that the GEM
methodology has the potential to overcome previous limitations, fulfilling the call from the
Organisation for Economic Cooperation and Development’s (OCED) Global Science Forum
for development of open-source risk assessment tools, highlighting the importance of this, and
subsequent research (Pinho, 2012).
The ideology underpinning pre-disaster planning is to minimise delay in providing commodities
and healthcare in order to reduce potential human suffering. With the Hyogo Framework of
Action (HFA) highlighting preparedness as one of the five priorities for action between 2005 to
2015 (Anhorn and Khazai, 2014; Balcik and Beamon, 2008; Yi and Özdamar, 2007).
Nevertheless, due to the unpredictability of natural disasters and response of the built
environment it can be inherently difficult to identify optimal locations for services. In this regard
several models have been developed in order to identify potential locations, including: a
dynamic logistics and coordination model for evacuation and support (Yi and Özdamar, 2007)
and a pre-positioning and dynamic delivery planning model (Rawls and Turnquist, 2012).
However, the majority of research implements simple metrics to infer building damage and
population displacement. Additionally, little research explores identification of suitable areas
for aid distribution, being one of the most important factors in the immediate aftermath of a
disaster (Anhorn and Khazai, 2014).
Study Site
Kathmandu is the capital and largest urban agglomerate of Nepal; the city is one of fastest
growing in South Asia (Fernandez et al., 2006; Mohanty, 2011). The Valley is centred
geographically in Nepal and is made up of three administrative districts: Kathmandu, Lalitpur
and Bhaktapur. There are five municipalities within the Valley, namely; Kathmandu
Metropolitan City (KMC), Lalitpur Sub-Metropolitain City (LSMC), Bhaktapur Municipality (BM),
Madhyapur (Thimi) Municipality (MM) and Kirtipur Municipality (KM) and 98 Village
Development Committees (Dixit et al., 2013). KMC is the largest municipality and obtains the
majority of Government offices. Whilst the entire Valley is considered the capital of Nepal, only
KMC was investigated for this project, drawing similarities from other research (Dixit et al.,
2013; Anhorn and Khazai, 2014).
4
To see an example of the IDCT please refer to the instructional video created for this project at:
https://www.youtube.com/watch?v=6GVjwJxmivM
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In the last century Nepal has only experienced two devastating earthquakes. A Richter Scale
Magnitude (M)8.1 earthquake occurred in January 1934 with an epicentre near the Indian
border, with the Kathmandu Valley experiencing intensities of IX-X on the Modified Mercalli
Intensity Scale (MMI). 8,519 people were reported dead, with 4,296 located in Kathmandu
Valley. A M6.8 earthquake occurred in August 1988, with an epicentre in Eastern Nepal.
Kathmandu Valley experienced an MMI of VII-VIII, with 721 deaths throughout Nepal (Dixit et
al. 2013). Current Global Positioning System (GPS) measurements indicate a convergence
rate of 20 ± 3mm/year (Bilham et al., 2001; Gupta and Gahalaut, 2014). Bilham et al., (2001)
divided the Central Himalaya into ten regions of 220km. Given the specified rate of
convergence six regions were identified to have a slip potential of at least 4m, equivalent to
the 1934 earthquake. However, owing to the historic record indicating no great earthquake
throughout the Himalaya since 1700 the slip potential may have increased to 6m in some
areas. Furthermore, due to the earthquakes of 1905 and 1934 not revealing surface ruptures
but warping river terraces and growing foothills, parts of the Himalaya may not have ruptured
for 500 to 700 years, generating a potential slip exceeding 10m in some areas. Thus,
aforementioned earthquakes would be considered atypically small, evidently highlighting the
need for pre-event vulnerability assessments for earthquake risk mitigation in Kathmandu
Valley.
Methodology
Before a ground survey for generating a building exposure dataset (or model) can be
commenced a building taxonomy must be defined. Unfortunately the majority of building
taxonomies have a regional or country-based focus, or only contemplate structural
components. Additional attributes surrounding general building information, non-structural
elements, occupancy, construction affecting earthquake performance and retrofit work all
contribute to building performance, considered by the GEM taxonomy. The GEM building
taxonomy portrays a unique building description, similar to a genetic code (genome). The
building genome is composed of 13 attributes, each representing a specific characteristic that
affects seismic performance.
A level 2 exposure dataset was generated through data aggregation into a mapping scheme,
being a statistically inferred distribution of building stock applied to homogenous zones.
Homogenous zones were delineated to the GEM sample 3 classification using Pléiades pansharpened 50cm orthorectified multispectral imagery of KMC provided by Astrium Services,
OpenStreetMaps (OSM), Google Earth and Panoramio.
Building outlines were extracted from the satellite imagery. However, within the urban
environment traditional pixel-based classification analysis presents multiple challenges owing
to different urban land types having a similar spectral reflectance (Erener, 2013; Myint et al.,
2011). Object-Based Image Analysis (OBIA) permits image division based on pixel groups
obtaining similar spectral and spatial properties, enabling superior classification in an urban
context (Blaschke et al., 2000; Myint et al., 2011; Wong et al., 2003). A Multiresolution
Segmentation Algorithm (MSA) employing a fractal net evolution approach utilising local
mutual best-fit heuristics to identify the least heterogeneous merge in the local area, following
a gradient of best fit was implemented (Blaschke et al., 2000). The non-parametric standard
nearest neighbour classifier was utilised for image classification, being advantageous where
spectrally similar classes are not easily separable (Myint et al., 2011)
Building characteristic surveys were collected through the IDCT on android tablet devices,
implementing the GEM Building Taxonomy (Jordan et al., 2014; Brzev et al., 2013). The Nepal
National Society of Earthquake Technology (NSET) agreed to recruit and train (based on
provided materials) 16 students for data collection with a sample size of 478 buildings.
Sampling was undertaken through allocating wards to students at the request of the NSET to
reduce travelling time after reviewing initial documentation that specified homogenous zone
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division. The sampling design tool produced by the National Oceanic and Atmospheric
Administration (NOAA) facilitated proportional building homogenous zone selection based on
the total number of required buildings (478). Some attributes of the GEM taxonomy required
in-depth building attributes to be obtained. Therefore, a document of tables was provided for
an expert from the NSET to complete in order to identify the most likely internal building
characteristics (i.e. lateral load resisting system) from visible external characteristics. Pléiades
multispectral imagery and OSM data covering the study area were processed to online
publishable tiles for compatibility compliance with the IDCT. Additionally 16 sample files
containing digital and printed sample locations for each student were provided and loaded onto
individual tablets.
The SIDD permitted generation of an exposure dataset through assignment of mapping
schemes, being a statistical summary of: construction type, internal building characteristics,
era and height defined by the GEM building taxonomy to homogenous zones (Hu et al., 2014).
It enables a simplification of complex processes that structural engineers undertake in order
to develop building exposure. For each type of homogenous zone the SIDD generates a
preliminary mapping scheme, with a subsequent iterative adjustment and addition of
secondary modifiers for zone characterisation (Hu et al., 2014).
Aid site selection followed logic by Anhorn and Khazai (2014), assuming a ‘worst case’
scenario in which aid location is exclusively based on open space. In order to identify initial
optimal aid locations, factors influencing aid locations were extracted from OSM, namely: major
roads, hospitals and schools. These factors were selected due to possible migration toward
public buildings in the event of an earthquake. Schools are commonly used for initial protection,
whilst hospitals treat the injured with people normally gravitating towards roads in an attempt
to reach services (Anhorn and Khazai, 2014). It is appreciated that the majority of roads may
be impassable, however major roads often have a clear buffer between buildings and the road
surface, potentially reducing the impact of rubble, permitting some sort of accessibility.
Following this a cost surface was produced based on defined homogenous zones. It was
assumed that one person could walk 3.6km per hour. Homogenous zones were reclassified
based on urban density due to possible collapse of urban features that could preclude direct
passage. Similarly, road types were also reclassified based on logic that major roads would
incur less direct damage. Consequently a cost surface was produced and input into cost
distance for: hospitals, schools and major roads. An average of the outputs was taken
generating an initial optimal aid map indicating average time to the three factors. Examination
of the satellite imagery for potential aid locations was undertaken through manual interpretation.
Extraction though image-processing software was contemplated, however due to the
heterogeneous characteristics of initial sites manual identification was preferenced. Potential
sites were selected based on the amount of open space and distance from buildings. The
mean value of the initial optimal aid map was extracted through zonal statistics as table.
Buildings were assigned a ward based on the shape file provided by NSET. The number of
buildings per ward was then extracted, with the population per ward obtained from the Nepal
Bureau of Statistics (NCBS) being divided by the number of buildings to produce average
number of people per building per ward. Furthermore, aid site capacity (number of people) was
identified following logic that one person requires 9m2 of space as stated in Anhorn and Khazai
(2014). A network dataset was then generated based on OSM roads. A subsequent maximum
capacity location-allocation problem was initiated, with buildings defined as demand points and
aid sites as facilities. This was considered more appropriate for immediate emergency aid sites,
covering as many possible demand points rather than minimising distance between supply
and demand (Anhorn and Khazai, 2014; Indriasari et al., 2010). Cost distance of unallocated
buildings permitted identification of aid sites in preferential locations for immediate aid
distribution in order to service the greatest number of unallocated people seeking aid.
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Implementation of the mapping scheme and exposure datasets permitted a more realistic
depiction of unallocated buildings and aid seeking population. Homogenous zones were
ranked based on building attributes. In the four most vulnerable homogenous zones it was
assumed all residents would seek aid, in the next seven half would seek aid, and in the final
three only a quarter would seek aid. The maximum capacity location-allocation problem was
re-run considering building vulnerability.
Results/Discussion
Delineation of homogenous zones highlighted the vast urban agglomerate that KMC presents;
with 75.67% of KMC being identified as residential (Bhattarai and Conway, 2010). Moreover,
when this was decomposed to the most detailed sample level; Moderate Residential density 2
(Mr) and High Residential density 2 (Hr) combine to represent 41.74% of the area within KMC.
Additionally, whilst core wards appear to have a relatively low population and relatively small
number of buildings; when the average number of people per building is computed core wards
exhibit an average between 22.40 and 29.31 people per building, highlighting the sheer volume
of residential areas, buildings and associated population within KMC, making the 2021 target
ratio of 40:60 of built to non-built seem somewhat unrealistic (Bhattarai and Conway, 2010).
Including a form of building exposure and subsequently altering population to be representative
of those seeking aid is exhibited in Figure 1 and Table 1 reducing unallocated population from
75.8% to 70%. Whilst this result is still concerningly high, preferencial aid sides identified in
Figure 1 and Table 1 have the potential to substantially reduce mortality.
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Ü
52
2819
3
26
38
61
56
6
20 55
11
69
73
13
32
42
40
24
4441
65
48 21
714 17 33
29 3634
51 46 50
63
53 57
62 58
59
31
37
45
25
10
1
4
8
9
2
23
22
16
70
67
18
30
5 35
49
47
27
12
66
68
7174
60
54
64
72
0
1.5
3
6 Kilometers
Coordinate System: WGS 1984 UTM Zone 45N
Projection: Transverse Mercator
Datum: WGS 1984
False Easting: 500,000.0000
Legend
False Northing: 0.0000
Central Meridian: 87.0000
Aid site suitability
Scale Factor: 0.9996
3.09 - 4.87
Latitude Of Origin: 0.0000
Units: Meter
4.88 - 6.89
Source: Esri, DigitalGlobe,
GeoEye, Earthstar
Geographics, CNES/Airbus
DS, USDA, USGS, AEX,
(minutes to factors) Population per building
1.93 - 2.93
2.94 - 4.42
6.90 - 8.81
4.43 - 5.32
8.82 - 10.35
5.33 - 5.90
10.36 - 12.18
5.91 - 6.92
12.19 - 13.98
6.93 - 7.86
13.99 - 16.67
7.87 - 9.98
16.68 - 20.07
9.99 - 14.33
20.08 - 24.14
14.34 - 22.39
24.15 - 32.36
22.40 - 29.31
Figure 1. Combination of unallocated buildings considering homogenous zone vulnerability and initial
optimal aid factors overlain on average population per building per ward. Numerically identifying the
five most important aid location ranks in black, the next five in umber, the next ten in red and the rest
in cantaloupe. © OpenStreetMap contributors
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Table 1. Statistical summary of Figure 1 for the top 20 aid sites, whilst also indicating site-ranking
movement compared to analysis not considering building vulnerability: (1, ) = moved 1 rank down,
(1, )= moved 1 rank up

ObjectID
Suitability
(movement)
Rank
Average
Average
Average
Average time
(average time
time to
time to
time to
to unallocated
to all factors
hospital
school
major roads
buildings
(mins))
(mins)
(mins)
(mins)
(mins)
37
3.09
1
3.76
6.16
1.00
1.44
41
3.41
2
2.65
6.13
1.40
3.43
54
3.72
3
4.84
5.52
2.33
2.12
38
4.33
4
3.74
9.39
1.19
2.99
42
4.87
5
12.25
3.03
2.38
1.76
55 (2, )
5.56
6
6.03
10.43
5.22
0.60
32 (1, )
5.59
7
7.46
9.47
0.98
4.39
39 (1, )
5.89
8
6.45
11.29
1.00
4.83
40
5.99
9
7.84
12.71
1.05
2.34
26 (1, )
6.58
10
9.45
10.76
4.51
1.56
57 (1, )
6.86
11
6.67
16.51
2.53
1.79
65 (1, )
6.89
12
15.12
7.66
2.02
2.71
49 (4, )
7.24
13
10.63
10.39
6.11
1.89
33 (4, )
7.56
14
10.58
12.19
1.27
6.24
36 (1, )
7.74
15
11.92
8.32
7.33
3.35
56 (1, )
8.07
16
17.87
4.63
9.14
0.59
48 (1, )
8.10
17
20.28
5.56
4.18
2.34
60
8.48
18
21.97
10.78
0.71
0.47
52 (2, )
8.70
19
12.61
9.54
10.84
1.83
30
8.81
20
26.86
4.06
3.65
0.70
Whilst Figure 1 identifies the most in-need aid sites in terms of unallocated population but also
equally considering aid site location, contemplation must be given to the ethics of humanitarian
aid. The primary aim of humanitarian assistance is to meet human needs and address human
suffering where found (Nilsson et al., 2011). However, aid distribution is based upon certain
factors that can result in unequal aid division and failure to distribute to the most vulnerable
citizens (Nilsson et al., 2011; Rahill et al., 2014). In this research only building vulnerability was
considered per homogenous zone, altering the population requiring assistance. Nevertheless,
social vulnerability is aimed at identifying disadvantaged population, being constrained by
economic and political capital (Geiß and Taubenböck, 2013; Walker et al., 2014). Walker et
al., (2014) presented a social vulnerability index for Victoria, Canada through weighting the
following factors: average income, seniors living alone, dependent population, single-parent
families, housing ownership, unemployment rate, education, recent movers and language
barriers (Walker et al., 2014). However, due to the NCBS only collecting raw population, social
vulnerability was unable to be computed. Regardless, factors implemented by Walker et al.,
(2014) would not directly relate to KMC owing to the complex set of culturally specific social
factors determined by individual perception and importance of each factor in reaching a
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decision to seek aid (Bhattarai and Conway, 2010; Khazai et al., 2012). Furthermore, social
vulnerability should contemplate a form of social capital. Rahill et al., (2014) explored the
contested role of social capital after the 2010 Haiti earthquake. During the immediate
aftermath, international aid only met a small proportion of the aid requirement. Social capital
became an integral part of aid distribution; on the one hand it permitted access to those in
need, whilst on the other hand it exacerbated social inequality. Consequently, citizens with the
least resources were neglected, clearly highlighting the importance of including social capital
within social vulnerability in determination of aid sites and earthquake risk studies, not
considered by the majority of authors (Ehrlich et al., 2013; Kircher et al., 2006; Mück et al.,
2013; Nastev, 2013; Pittore and Wieland, 2013; Ploeger et al., 2010; Wieland et al., 2012).
To mitigate potential devastation after a major earthquake a paradigm shift in the very concept
of humanitarian aid is required. In this theme humanitarian aid needs to consider pre-event
assistance in the form of physical vulnerability, social vulnerability and sociological factors
surrounding earthquakes. In order for this to occur, humanitarian aid donors themselves need
to overcome the sociological factors of aid giving. Consequently for progress to be made the
Global society must change from a response-based driven approach to a prevention-based
strategy (Zhang et al., 2012). Regardless, it is appreciated that difficulties can be encountered
when distributing pre-event aid owing to potential corruption and misuse (Bhattarai and
Conway, 2010). Therefore it is of vital importance that appropriate frameworks and rigorous
checking measures are in place in order for successful pre-event aid distribution (Tucker, 2013;
Zhang et al., 2012).
Study Advancements
The presented project utilised the generated exposure dataset in a rather simplistic manner;
however, the sheer amount of attributes collected within the exposure dataset facilitates broad
project expansion with earthquake models. Nevertheless current earthquake models often only
have country specific applications and estimate economic and human loss based on pastearthquake events applied to the modern day population. The soon to be released OpenQuake
earthquake model attempts to remediate current issues precluding effective analysis through
the creation of a global earthquake model that is homogenous as possible, considering
epistemic uncertainties (Pinho, 2012).
OpenQuake is currently split into two modules: hazard and risk. The hazard module permits
users to identify a potential earthquake considering factors such as: earthquake magnitude,
rupture models, soil conditions, ground acceleration, peak ground acceleration and spectral
acceleration (Crowley et al., 2014). Following this the risk model entails five calculation
workflows: two calculate losses and damage distributions form a single earthquake, two
calculate seismic risk using probabilistic seismic hazard and a final one contemplates the
viability of retrofitting (Crowley et al., 2014). Specific calculations in the risk model run on the
output from a mixture of the: hazard component, exposure model, fragility model and
vulnerability model. Based on the differing scenarios currently available in OpenQuake outputs
include: loss statistics, loss maps, damage distribution estimates, collapse maps, loss
exceedance curves, retrofitting benefit/cost ratio maps, loss disaggregation and event loss
tables (Crowley et al., 2014). These output products would enable improved quantification of
potential optimal immediate aid sites based on enhanced identification of building damage and
collapse resulting in a more accurate depiction of displaced population.
Conclusion
Pre-event vulnerability is an emerging discipline requiring an exposure dataset, vulnerability
model and defined hazard (Pittore and Wieland, 2013). Owing to the lack of building stock
data, remote sensing is poised to be an essential tool within this discipline, with the GEM
toolset attempting to facilitate effective decision making through improved understanding.
Immediate aid sites are one of the most important factors in the immediate aftermath of a
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disaster, despite little research exploring potential optimal locations. Through combining an
initial vulnerability assessment with building footprint extraction and aid location suitability
analysis this research presents a novel insight into the potential devastation that Kathmandu
could face. Future outputs from the GEM are able to directly influence policy through
identification of buildings and areas at most risk from collapse, considering the earthquake
specific GEM taxonomy. Identified locations can then be prioritised for structural retrofit work
and aid distribution. The GEM tools enable all countries facing earthquake risk to undertake
risk assessments based on common, comparable standards, improving understanding,
identifying areas of concern and reducing mortality.
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