Proceedings, 6th
International Conference on Cartography and GIS, 13-17 June 2016, Albena, Bulgaria
ISSN: 1314-0604, Eds: Bandrova T., Konecny M.
834
ELECTRONIC REGIONAL RISK ATLAS: DEVELOPMENT,
STRUCTURE AND APPLICATION PRACTICE IN REPUBLIC OF
ARMENIA
Arshavir Avagyan, Radovan Hilbert, Hayk Yeritisian, Hasmik Manandyan,
Aleksandr Arakelyan, Artak Piloyan, Anna Elbakyan
Ph.D., Arshavir Avagyan; Institute of Geological Sciences of the NAS of RA; 24a M.Baghramyan
ave., Yerevan, Armenia; +374 10 564504; avagyan@geocom.am
Ph.D., Radovan Hilbert; ConsultITPro s.r.o.; Dolná 27, 96901, Banská Štiavnica, Slovakia;
radovan.hilbert@gmail.com
M.Sc., Hayk Yeritisian; Institute of Geological Sciences of the NAS of RA; 24a M.Baghramyan
ave., Yerevan, Armenia; hayk.yeritsian@gmail.com
Ph.D., Hasmik Manandyan; Institute of Geological Sciences of the NAS of RA; 24a M.Baghramyan
ave., Yerevan, Armenia; hayuhin@yahoo.com
Ph.D., Aleksandr Arakelyan; Institute of Geological Sciences of the NAS of RA; 24a
M.Baghramyan ave., Yerevan, Armenia; alex.arakelyan@outlook.com
M.Sc., Artak Piloyan; Masaryk University, Faculty of Sciences, Department of Geography;
Kotlarska 267/2, bld. 4, 611 37 Brno, Czech Republic; +420 607 619 337;
artakpiloyan@mail.muni.cz
M.Sc., Anna Elbakyan; Institute of Geological Sciences of the NAS of RA; 24a M.Baghramyan
ave., Yerevan, Armenia; anushelbakyan@gmail.com
Abstract
Initially ERRA was developed as a web-service presenting natural (landslides, flooding, earthquake, wildfire, strong
winds) and man-made hazards and risks.
Further elaboration of ERRA resulted in development of methodological, analytical and informational environment for
using spatial data in scientific research in Earth sciences and related disciplines. Spatial Data Infrastructure (SDI) has
been developed, which includes:
 Spatial data for Armenia and partially for neighbouring countries. These include spatial datasets of Armenia at
scale 1:100000 and larger (basemaps, administrative, geological, geomorphological, hydrological, land use,
environmental layers),
 Set of research methods and applications, such as multi-factor mapping of occurrences of natural events and
phenomena (landslides, erosion processes, critical discharge of surface waters) through overlay of spatial layers.
 SDI for scientific applications
 GIS for creating and publishing web maps and GeoPortal for providing access to spatial data through meta-data
catalogues.
 Methodology and software for carrying out research on spatial objects in GIS environment.
Keywords: GIS, Hazard assessment, mapping, web mapping, cartography, disaster management
INTRODUCTION
Digital maps, map-based information systems, simulations, and web-technology are increasingly applied in emergency
management (EM) [Maclachlan et al. 2007, Hagemeier-Klose and Wagner 2008, Haubrock et al. 2008]. In this respect,
spatial data and related technologies such as GIS, GPS, remote sensing (including visible, thermal and LIDAR
imaging), photogrammetry and web mapping are crucial for effective EM. In fact, without spatial data, one cannot
expect effective and efficient disaster management [Amdahl, 2002].
Proceedings, 6th
International Conference on Cartography and GIS, 13-17 June 2016, Albena, Bulgaria
ISSN: 1314-0604, Eds: Bandrova T., Konecny M.
835
Investigations have shown that various user-needs for EM via Web exist [Rohrmann 2004]. Essential aspects are the
design of visual components, easily understandable content, the offer of further information and a self-explaining, easy
to use interface [Hagemeier-Klose and Wagner 2009]. In Sheppard and Blatchford [1995], the main benefits of visual
information for EM are described:
 Speed of information access for the user.
 Provision of images (maps, photographs, video, diagrams, etc.) which allow people unfamiliar with the
location to obtain a fuller more realistic picture.
 Cross-cultural, language, and professional discipline boundaries.
 Provision of a rich source of quantitative data and chronological documentation or updates.
This paper aims to describe the development of SDI conceptual model and a prototype entitled “Electronic Regional
Risk Atlas” (ERRA) that facilitates spatial data collection, access, dissemination, management and use for proper
emergency management.
The electronic regional risk atlas of the Republic of Armenia (ERRA) was developed as a set of maps, linked with each
other by similar themes and methodology for assessing features of mapped objects and methods.
The functionality of atlas is organized as a web service, providing access to the spatial content through Geo-portal as it
is defined in Inspire Directive Article 11 [Directive 2007/2/EC, 2007]. Other functions of Geo-portal (metadata, spatial
data services and large spatial data sets) are also organized according to these requirements. Along with the layers, the
development of which is described in this article and base topographic and geographic datasets, ERRA includes spatial
information representing relief, geological structure, vegetation, soil coverage, water resources and climate for the
country. Also, data describing demographic, social, economic situation of the country has been used. Thus, during the
process of Atlas development the main components of country infrastructure for spatial information have been created.
Existing spatial datasets of ERRA are mostly developed by authors of this article and it allows planning a future
extension of thematic and application areas of Atlas. Spatial data organization for scientific research in field of Earth
Sciences, natural resources, environmental studies and other related topics are the core datasets. But the main content of
this article is dedicated to description of hazard and risk assessment and mapping of main disasters of Armenia using
ERRA functionality and GIS applications.
Structure of Atlas (ERRA Skeleton) has been developed by an expert team during the first phase of EU-funded
Programme for the Prevention, Preparedness and Response to Man-made and Natural Disasters in the ENPI East
Region (PPRD East).
SPATIAL DATASETS OF ERRA
Besides the hazard and risk maps, ERRA contains a number of other spatial datasets of Armenia, which are grouped as
follows:
Group 1. Basic Maps
 Administrative - State borders of Armenia, Marz borders, District borders, Settlements, Demography;
 Infrastructures - Railways, Roads
Group 2. Maps of natural conditions
 Geomorphology and geomorphometry – Geomorphology zones, DEM, Slope, Aspect, Elevation zones,
landscape types
 Hydrology - Main river basins, River network, Lakes, Reservoirs
 Geology
 Soil types
Group 3. Maps of natural hazards and risks
 The earthquake hazard and risk thematic layers – Recent earthquakes, Historical earthquakes, Seismic zones
 Landslide hazard and risk layers – Landslide location, Landslide slope, Landslide risk map, Land use in landslide
areas, Landslide bed rock geology, Landslide hazard per region.
Proceedings, 6th
International Conference on Cartography and GIS, 13-17 June 2016, Albena, Bulgaria
ISSN: 1314-0604, Eds: Bandrova T., Konecny M.
836
 Flood hazard and risk layers – Historical flood events (point and polygon), Embankments, Restrictions to water
flow (canals), Flood hazard by river basins, Flood hazard by regions, Flood hazard by marzes.
 Forest fire hazard layers – Wildfire hazard in June, July and August, Wildfire hazard distribution by elevation
zones, Wildfire hazard distribution by forest types.
Group 4. Industrial events hazard and risk
Technological event hazard and risk layers – Location layers of installations which may trigger risks, Technological
event hazard layers and risk layers by regions, Industrial event hazard layer by administrative divisions (marz).
Group 5. Critical Infrastructures
Spatial data for the following assets: Administrative buildings, Airports, Health facilities, Roads, Tunnels, Bridges,
Railway, Power plants, High–voltage power lines, Electric substations, Telecommunication stations, Industrial
buildings, Water use, Water extraction, Water discharge, Drinking water, Ore deposits, Chemical factories, Petrol
stations, Reservoirs, Tailing dams, Solid waste, ARS divisions, Fire rescue centres, Mines, Enrichment plants.
Group 6. Regional Disaster Vulnerability Index
Vulnerability index layer by marz – Vulnerability index layer, Social vulnerability layer
Group 7. Natural resources
Non-metallic deposits, Ore deposits (operated), Ore occurrence, Tailing dams, Fresh drinking groundwater deposits,
Hydro-geological wells, Soil types, Landscape types, Forests.
In order to achieve interoperability of ERRA with other Geo-portals and support services, each of layers is equipped
with Metadata according to ISO 19115/19119/19139 standards. Discrete units of metadata are: map title, keyword,
spatial resolution, responsible organization, source, author(s) and short description.
METHODOLOGY OF HAZARD AND RISK ASSESSMENT AND MAPPING
Methodology Summary
The extent of mapped hazards and risks in all of the resulting layers included in Atlas depends on a number of coinfluencing
factors of natural and man-made character. Hence, for the development of hazard and risk (as spatial
variables) maps, a spatial multi-criteria evaluation method have been used. The theoretical background of this method is
based on Analytical Hierarchical Process (AHP) developed by Saaty [Saaty, 1980].
This method allows to estimate hazard and risk quantitatively using the following principle: Hazard/risk formation
factors are presented as a set of raster layers, and each of these layers’ grid-cells are presented with specific
standardised mean of factor. Then target layers of hazard/risk are created by summation of weighted means of criteria in
spatially corresponding grid – layers of all cells [Van Westen et al., 2013].
In some cases target variable (hazard/risk) is caused by one or more compound factors which depend on subfactors. In
this case a criteria “tree” is compiled. An example of this is the calculation of vulnerability index which consists of five
sublevels.
Weighting coefficients which were used in described example were derived by experimental method and need to be
continuously verified by correction of hazard/risk formation factors.
In this article the following methodologies are described:
 Flood, landslide, wildfire hazard assessment and mapping;
 Development of specific technological event hazard layer
 Asset characterization and mapping
 Assessment and mapping of population / community vulnerability
Proceedings, 6th
International Conference on Cartography and GIS, 13-17 June 2016, Albena, Bulgaria
ISSN: 1314-0604, Eds: Bandrova T., Konecny M.
837
Spatial multi-criteria evaluation
The input data are a set of maps spatially representing the criteria, which subsequently were grouped, standardized and
weighted in a “criteria tree”. The outputs are “composite index map(s)”, which indicate the realization of the model
implemented.
The value of each cell in the matrix is composed of the multiplication of the standardized value (between 0 and 1) of the
criterion for the particular alternative, multiplied by the weight (W1 to Wn) related to the criterion. Once the matrix has
been filled, the final value can be obtained by adding up all cell values of the different criteria for the particular
alternative (e.g. a11 to a1n for alternative A1).
For implementing this matrix according to the AHP, three important steps need to be considered. The first one divides
the problem (and the weights) into a hierarchical structure. The second one considers the weighting process, employing
the pairwise comparisons of the criteria, and the synthesis is related to the multiplications among the hierarchical levels.
Additionally, in the spatial implementation of this procedure, every criterion (Cj) becomes a raster layer, and every
pixel (or set of pixels) of the final composite index map eventually becomes an alternative Aj. The goal (risk index) has
been decomposed into criteria levels CL1 and CL2
Flood Hazard Assessment and Mapping
This methodology has been developed to evaluate the flood hazard assessment within country based on the principles
defined within the Flood Directive [DIRECTIVE 2007/60/EC, 2007]. The purpose of the EC directive is to establish a
framework for the assessment and management of flood risks, aiming at the reduction of the adverse consequences for
human health, the environment, cultural heritage and economic activity associated with floods in the Community. This
methodology has been developed to consider the initial stages of the directive, and will allow flood prone areas to be
identified and mapped onto a GIS.
The objectives of this methodology are to:
 Estimate the potential flood hazard based on hydro-meteorological, geo-ecological and geomorphometric
parameters,
 Determine the settlements with high potential flood risk based on evaluation of past flood events,
 Develop flood hazard map / GIS layer.
Fig. 1. Flood Hazard Assessment and Map Development Flowchart
Geological and Environmental
Description of Basins
Analysis of Hydrologic and
Climatic Characteristics of
Basins
Historical Inventory of Flood
Events
Description of Flood
Protection Infrastructure
Assessment of Geo-Ecological
Flood Hazard
GIS data on geological features, geological condition and
permeability of the ground, land use, areas with drinking water,
with water for recreational use, water suitable for fish production,
protected areas.
Excel tables with hydrographic data for the main hydrological
posts of 14 basins: monthly and annual average flow, m3
/s;
annual average flow module, l/s*km2
; maximum flow, m3
/s, date;
minimum flow, m3
/s, date. Դata on actual climatic conditions (air
temperature, precipitation, relative humidity, snowcover) and
forecast for impact of climate change on the basin climate.
GIS shapefile and Excel table of past floods inventory (including
information on Date of the event; Basin ID; River name;
Settlement ID; Settlement Name; Coordinates of location area
affected; Economic losses; Human lives lost; Displaced people;
Short description of flood event)
GIS shapefiles and Excel tables of: Canals; Reservoirs;
Embankments; Lakes.
GIS layers/maps of soil types and geomorphology (Permeability
coefficient value (High, Impermeable rocks, low); geology
(Carbonaceous rocks, silicate rocks))
Delineation of basins
with area >500km2
Description and
analysis of basins
GIS and tabular
datasets development
for flood hazard
assessment
Multi-factor analysis
of basin flood hazard
FLOOD HAZARD
MAP
Proceedings, 6th
International Conference on Cartography and GIS, 13-17 June 2016, Albena, Bulgaria
ISSN: 1314-0604, Eds: Bandrova T., Konecny M.
838
The results of this methodology are:
 A register of locations on rivers where there is a known probability of a floods within the defined structure,
 Description of basins,
 Metadata records about all existing hydrologic models available in country along with other flood maps,
 Hazard map based on the register and analysis of records,
 Understanding of flood hazard potential based on morphometric and geo-ecological characteristics.
Necessary data for flood hazard assessment has been collected for the 14 basins of Armenia with an area of more than
500km2
. As a result of applying this methodology, these basins have been classified by the flood hazard level into 4
categories: extreme, high, middle, low.
Flowchart of flood hazard assessment and mapping procedure is presented below (Fig. 1).
Landslide hazard map/ GIS layer development
The methodology used by authors (Georisk Company) to prepare the landslide hazard map of the territory of Armenia
provides an index approach based on the selection of specific factors that lead to landslide activation. The core of this
method is the combined analysis of factors of different nature and value, which requires one to standardize them and
convert to values ranging from 0 to 1. The following factors are considered for landslide Hazard assessment: Geology,
Seismicity, Slope Gradients, Precipitation and Land use. The standardized values for each of these factors, and the GIS
layers derived by these values, are presented below (Tables 1 to 5).
Table 1. Slope gradients (in degrees) and standardized values assigned to them
Slope
Gradients
0-10 10.1-20 20.1-30 30.1-40 40.1-50 50.1-60 60.1-70 70.1-80 80.1-90
Standardized
Values
0.1 0.1 0.1 0.1 0.2 0.5 1 0.8 0.1
Table 2. Land use classes and standardized values assigned to them
Land use
Yerevan
city
Arable
land
Crops Pastures Grassland Forest,
shrubland
Lake
Sevan
Reservoir
Standardized
values
1.000 0.500 0.500 0.181 0.181 0.065 0.042 0.042
Table 3. Geological complexes, their description and standardized values assigned to them
Geological
complex/
Description
A. Strongly
weathered
fractured
formations
of rocks
B. Formations
with sandstone,
conglomerates,
clays and marls,
both weathered
and fractured
C. Ultra-mafic
rocks, tectonically
discontinuous,
lutites, calcarenites
and aleurolites
D. Different
lithology in the
formations that
have been well
preserved and
contain rocks less
affected
tectonically
E. Massive and
compact
formations,
Quaternary
sediments in
plain areas.
High-resistant
volcanic rock
Standardized
Values
1.000 0.565 0.348 0.196 0.089
Table 4. Maximum amounts of daily precipitation and the standardized values assigned to them
Amount of
precipitation in mm
40 45 50 55 60 65 70 75 80 85
Standardized values 0.44 0.50 0.55 0.61 0.66 0.72 0.77 0.83 0.88 0.94
Table 5. Peak ground accelerations (pga) and the standardized values assigned to them
P.g.a., in fractions of g 0.1-0.2g 0.21-0.3g 0.31-0.4g 0.41-0.5g
Standardized values 0.3 0.5 0.7 0.9
Proceedings, 6th
International Conference on Cartography and GIS, 13-17 June 2016, Albena, Bulgaria
ISSN: 1314-0604, Eds: Bandrova T., Konecny M.
839
Landslide hazard index is calculated as a weighed sum of standardized values of considered factors as presented in the
expression bellow:
Landslide Hazard index=0.8(0.5(Slope gradient) +0.2(Land use) + 0.3(Geology)) + 0.2(0.7(Rains) + 0.3(Earthquake)).
Name of factors in parenthesis refers to their standardized value in the same grid cells of appropriate thematic layers.
At the last stage, all calculated values of the landslide hazard index are reduced to the three values of “high”,
“moderate” or “low” for creation of resulting layer for ERRA.
The analysis of results produced for the territory of Armenia demonstrates a good correlation between the Map of
Landslide Hazard and the distribution of landslides. In the meantime, the correlation shows that geological structure and
seismic impacts have determinative influence on landslide activation. Slope gradient is a less important factor and
landslides associated with it are mainly those surrounding the Aragats mountain. Precipitation has even smaller effect,
which is explained by its low rate, considering that Armenia is situated within an arid climatic zone. The impact of land
use is almost indiscernible, which might be explained by the small scale of 1:500,000, which doesn’t include any manmade
features.
Development of Wildfire Hazard Map
Country wildfire hazard as a thematic layer of ERRA has been created by developing sub layers and using the
appropriate weighting scores. The information used for base – layer development covers data on Population
distribution, Land use, Topography (Digital Elevation Models), Transportation networks, River channels and basins.
Methodology of creating the wildfire hazard layer identifies individual fire hazard factors and groups them in sub-layer
which share similar characteristics. The sub-layers developed are static in nature and conform to preliminary regional
base model for Armenia. These are fuel condition and weather triggers. A final fire hazard output is produced by
combining these sub-layers using a weighted approach.
In order to construct the static wild fire hazard map the variables for each sub-layer are converted into a hazard index.
The hazard factor obtained has been normalized with standardized values varying between 0.00 to 1.00. For the territory
of Armenia, the following variables have been used to develop the equations below:
Fuel hazard = 0.54(Fuel type) + 0.32 (Slope) + 0.09 (Aspect) + 0.06 (Elevation); Weather hazard = 0.74 (Temperature)
+ 0.26 (Precipitation) and finally Wild fire hazard = 0.81 (Fuel) + 0.19 (Weather).
The fuel hazard type takes into account land cover classified by the International Geosphere-Bioshere Programme
(IGBP) on 16 land cover or fuel type – each one with specified hazard standardized value.
Table 6. Land cover classification and hazard indices according to International Geosphere-Bioshere Programme
Land Cover
Type
Evergreen
Needle
leaf
Forest
Evergreen
Broadleaf
Forest
Deciduous
Needle
Leaf
Forest
Deciduous
Broadleaf
Forest
Mixed
Forest
Closed
Shrub
land
Open
Shrub
land
Woody
Savannahs
Hazard
Index(0-low,
16-high)
11 10 15 12 10 15 14 9
Standardized
Value
0.688 0.625 0.938 0.750 0.625 0.938 0.875 0.563
Savannas Grassland
Permanent
Wetland
Croplands
Urban and
Built up Lands
Cropland/
Natural
Vegetation
Mosaic
Snow
and
Ice
Barren
Water
Bodies
8 16 1 4 3 5 0 2 0
0.500 1.000 0.063 0.250 0.188 0.313 0.000 0.125 0.000
The slope hazard index has been established according to several rules related to wildfire behaviour. Since steep slopes
tend to increase the rate of fuel burning, higher values will be given to those, compared with flat surfaces or lower slope
gradients. Slope hazard fire index is based on the following classification and standardization. Slope gradient from flat
up to 40 degree and more was divided into 9 classes from very low up to very high fire hazard probability. Classes have
been assigned to hazard indices and then their values were standardised from 0.022 – very low to 0.8 high.
Proceedings, 6th
International Conference on Cartography and GIS, 13-17 June 2016, Albena, Bulgaria
ISSN: 1314-0604, Eds: Bandrova T., Konecny M.
840
The aspect factor is the most important because it has effect on wind conditions, air moisture and is related to how dry
the fuel is and spread of fire.
Four Aspects have been considered for this layer: North, West, East and South with standardized value of hazard index
from 0.1 to 1.0.
The elevation impact is based on the following assumptions: higher elevations are known to experience greater rainfall
and an impact on the vegetation type, humidity and temperature. Five Hazard classes have been defined as a follows:
Table 7. Elevation ranges and fire hazard indices
Elevation (m ) 0 – 483 484 – 1074 1075 – 1709 1710 – 2435 >2436
Fire Hazard High Medium High Medium Medium Low Low
Fire Hazard Index 5 4 3 2 1
Standardized value 1.000 0.667 0.200 0.400 0.067
Weather factors have influence on fuel moisture and fire spread. The main factors are temperature and precipitation
which are considered for fire hazard assessment.
Table 8. Temperature hazard indices
Temperature (Cº) 0 – 3.2 3.3 – 6.4 6.5 – 9.5 9.6 – 12.9 >12.9
Weather Hazard Low Medium Low Medium Medium High High
Weather Hazard Index 1 2 3 4 5
Standardized value 0.067 0.200 0.400 0.667 1.000
Precipitation hazard index is considered according to its average per month values:
Table 9. Precipitation hazard indices
Precipitation (mm) 429-685 686-861 862-992 993-1,180 >1.181
Precipit. Haz. index Low Medium Low Medium Medium High High
Weather Hazard 5 4 3 2 1
Standardized value 1.00 0.67 0.4 0.20 0.67
Based on the described model, wildfire hazard layers per month for June, July, August, and layers of hazard
distribution by elevation zones by forest types have been created.
Development of Technological Event Hazard Layer
According the EU Council Directive 67/548/EEC, hazardous installations are divided to (a) explosive, (b) oxidising, (c)
easily flammable, (d) flammable, (e) toxic, (f) harmful, (g) corrosive classes. For the development of Technological
Event Hazard Layer, potentially hazardous installations which currently operate in Armenia have been identified,
classified according to mentioned-above directive and the final spatial data compiled.
In case of man–made factors Industrial accident (e.g. fire, explosion, mechanical failure), Collision, Human error,
Systems failure are considered as possible triggers.
Landslide, avalanche, flooding, earthquake and wildfire have been considered as natural disaster events that could
impact the installation considered. Statistical and spatial data on natural hazard was analyzed to assess the risk level of
installation location.
The following categories of baseline data have been identified for each installation which are assessed and mapped as
hazardous.
1. Site Setting Category includes information on:
a) Geographic boundaries of that hazard, i.e. how far could the hazard extend in the case of a technological event.
Different accident scenarios have been considered, such as: vessel rupture, pipe rupture, leak, fire, explosion,
installation security (e.g. unauthorized personnel entry).
Proceedings, 6th
International Conference on Cartography and GIS, 13-17 June 2016, Albena, Bulgaria
ISSN: 1314-0604, Eds: Bandrova T., Konecny M.
841
b) Existence of receptors at potential risk within anticipated boundaries of hazard. Existence of a credible hazard
transport mechanism, such as air (wind direction, strength), surface water (rivers, canals, sea, drinking water
supply) and groundwater.
c) Recurrence of a technological event by available qualitative or quantitative method.
d) Existence of evidence that installation owner/operator assesses consequences of industrial event.
e) Existence of evidence that installation owner/operator assesses measures preventing from industrial events.
The compiled information on Baseline Installation Categories have been classified according to intrinsic Hazard level
(Table 10) and Frequency of Occurrence of Specific Type of installation (Table 11)
Table 10. Hazard Categories by Installation type
Category name
Hazard
level
Hazard
index
Food, Beverage, Tobacco Products, Electrical and Electronics, Municipal solid waste Low 1
Cement Works, Airports Medium 2
Oil and gas processing, Animal and Animal Products, Metal Product Manufacturing,
Electricity Generation
Fair
3
Organic and Inorganic Chemical Production, Mineral Extraction and Processing,
Hazardous Waste Water supply, Mineral Extraction and Processing
High
4
Table 11. Frequency of Occurrence of Specific Type of installation
Category Type name
Frequency of Occurrence of
Specific Type of installation
Frequency Index
of Specific Type
of Installation
Airports, Metal Products Manufacturing, Mineral Extraction
and Processing, Water supply
Once in 50 years 0
Organic and inorganic Chemical Production Once in 20 years 1
Hazardous Waste, Electricity Generation,
Cement Works
Once in 10 years 2
Oil and gas processing, Food, beverage, tobacco products,
Electrical and Electronics, Animal and Animal Products
Once in 5 years 3
Municipal solid waste Once a year 4
2. Risk Management Category includes the following information:
a) Existence of formal, documented Risk Assessment for each installation.
b) Planned and implemented measures in place to limit and mitigate consequences of an event
c) Training plans of personnel in the organization, responsibilities and emergency response procedures described
and training conducted.
3. Preparedness and planning measures describing prevention policy:
a) Existence of a Major Accident Prevention Policy for the installation;
b) Existence of a Safety Management System for the installation;
c) Existence of an Emergency Plan for the installation.
4. Conclusion for each installation.
Consideration of the above for each installation would indicate if:
a) An installation could present a potential hazard;
b) There is a credible set of conditions which could trigger a Technological Event;
c) Technological Event could adversely impact vulnerable receptors;
d) The installation Owner/Operator has put in place hazard mitigation measures which would eliminate or
significantly reduce the impact of the event on the receptors
The preparedness extent of potentially hazardous installation by Baseline Data is shown in table 12.
Proceedings, 6th
International Conference on Cartography and GIS, 13-17 June 2016, Albena, Bulgaria
ISSN: 1314-0604, Eds: Bandrova T., Konecny M.
842
Table 12. Installation Preparedness
Preparedness Baseline Data description
Preparedn
ess Level
Prepar
edness
index
Complete absence of document on disaster risk reduction None 0
Existence of regional plan of action in an emergency situation Poor 1
Existence of regional plan of action in an emergency situation, Certificate of technical
safety
Faire 2
Existence of regional plan of action in an emergency situation, Certificate of technical
safety, Own plan of action
Good 3
Existence of regional plan of action in an emergency situation, Certificate of technical
safety, Own plan of action, Emergency Drills regularly practiced
Prepared 4
By summing up the factors according to tables 1, 2 and 3, industrial event hazard for an installation is
calculated:
Installation Hazard = 0.4*[Installation Type Hazard Index] +0.2* [Occurrence Frequency Index of Specific
Type of Installation] - 0.4*[Preparedness Index]
Using the above-mentioned equation, a value of technological hazard was assigned to each marz, which is equal to
summary of hazard values for installations, which are located in each marz.
Asset characterisation and mapping
Asset characterisation and mapping was developed for assessment of national critical infrastructure and classification of
asset elements at natural and man-made disaster risk. The purpose of characterization is identification of an asset and to
determine the level (degree) of its importance to country’s economy, environment and social well-being.
Individual assets are specified by following attributes: asset ID, Brief description, Category name (one of 9 categories),
spatial type (point, line, area), coordinates, Perceived criticality or importance ((0) - None, (1) - Very low, (2) - Low, (3)
- Medium, (4) - High, (5) - Very High), Interdependencies (Yes or No), Number of Interdependencies ((Not known), 1-
8)), Number of dependencies ((Not known), 1-8)), Sub-assets (Does the asset have any crucial elements (Y/N)),
Network (Is the asset being characterised a crucial part of a network comprising an important system (Y/N)),
Consequence types (The type of direct consequence that would occur if asset were lost (top 3 only, 1-highest, 2-second,
3-thrid); Financial, Environmental, Life), Susceptibility to hazards (Asset susceptibility or vulnerability to hazard type
(Y/N), Qualify susceptibility (For Each susceptible hazard selected the following degree of susceptibility 0) Negligible,
1) Low, 2) Moderate, 3) High), Hazard intensity required to significantly disrupt damage asset (Level or intensity of
each hazard required to damage or cease functionality of the asset 1) Any, 2) Low, 3) Moderate, 4) High, 5) Very high),
Ease of recovery (Following the degree of hazard above detail the maximum possible time to recovery or acceptable
working level for each selected hazard 1) Week, 2) Month, 3) 3 Month, 4) 12 Month, 5) >12 Month, 6) Never).
Wherever Y/N answer was requested, it was replaced by 1/0. The following natural hazards were selected as specific to
Armenia and assessed in format requested by PPRD: Earthquakes, Landslides, Floods.
Each of these hazards is given a specific field for Susceptibility to Hazard, Qualify Susceptibility, Hazard intensity
required to significantly disrupt/damage asset and Ease of recovery categories. Hence, each category is divided into
additional 3 fields for each hazard.
The final result of this exercise is an ESRI Geodatabase and an excel sheet with the list of all critical assets and
mentioned-above variables.
Proceedings, 6th
International Conference on Cartography and GIS, 13-17 June 2016, Albena, Bulgaria
ISSN: 1314-0604, Eds: Bandrova T., Konecny M.
843
Assessment and mapping of population / community vulnerability
There are several definitions of vulnerability; therefore, there are various approaches of its assessment and mapping.
Comparative analysis of widely used methods and approaches of vulnerability assessment has been carried out to select
the most appropriate method.
The International Federation of Red Cross and Red Crescent defines vulnerability as “The characteristics of a person or
group in terms of their capacity to anticipate, cope with, resist and recover from the impact of a natural or man-made
hazard” [Guidelines for emergency assessment, 2005]. Vulnerability and Capacity Assessment (VCA) method which
uses participatory approach to assess people’s exposure to and capacity to resist natural hazards
(http://www.ifrc.org/en/what-we-do/disaster-management/preparing-for-disaster/disaster-preparedness-tools1/) is based
on this definition. According to the United Nations International Strategy for Disaster Reduction (UNISDR),
vulnerability is “The characteristics and circumstances of a community, system or asset that make it susceptible to the
damaging effects of a hazard” [UNISDR, 2009]. The approach of the UNISDR) [ISDR, 2004] considers physical,
social, economic, and environmental factors of vulnerability, which increase the susceptibility of a community to the
impact of hazards. This approach of vulnerability assessment as a susceptibility of a system to hazards does not include
coping capacities or exposure, being separate elements that contribute together to produce risk [N. J. Roberts et. al.,
2007].
EU Framework Programmes, related to management of risks and emergency situations due to natural and technological
hazards, consider quantitative and qualitative approaches of risk assessment [Wood Maureen and Jelínek Róbert, 2007].
Therefore, vulnerability presents:
 The degree of loss (from 0% to 100%) resulting from a potentially damaging phenomenon in the quantitative
(probabilistic) approach of risk assessment;
 The degree (e.g. high, medium, low) to which a system is susceptible and unable to cope with damage or harm
in the qualitative approaches of risk assessment.
The qualitative approach of vulnerability assessment and mapping is presented below. It provides opportunity for both
visualization of spatial distribution of population vulnerability at regional level, and use of the vulnerability map for
natural and technological hazards’ risk assessment and mapping. This approach considers vulnerability as a logical
combination of three indicators – susceptibility, coping capacity and adaptive capacity. Each indicator (component) of
vulnerability consists of several subcomponents representing a factor of vulnerability.
Subcomponents of vulnerability are combined by means of application of weights (as values of 0 to 1) to represent a
component. Similarly, components are combined using weights for quantitative estimation of overall vulnerability
index.
Values of weights are determined by expert judgment, and they need to be verified later. Fig. 2 represents the weights of
different components of the vulnerability index.
Fig. 2. Weighting values of vulnerability index and its components
Proceedings, 6th
International Conference on Cartography and GIS, 13-17 June 2016, Albena, Bulgaria
ISSN: 1314-0604, Eds: Bandrova T., Konecny M.
844
Susceptibility is determined by public infrastructure, housing conditions, poverty and dependencies. Coping capacity is
determined by unemployment rate and economic coverage (land ownership and diversification of household income).
Adaptive capacity is determined by education level and gender equality. Eventually, the vulnerability is assessed
quantitatively and represented by an index varying from 0 to 100%. The value of this index is used for mapping
population vulnerability at regional level.
Data collection from different governmental, public and private organizations has been implemented. Collected data
have been compared and assessed based on their quality, reliability and relevance. Consequently, the most reliable and
up to date data have been selected and organized as a database for vulnerability assessment and mapping.
A vulnerability relational database has been developed. The database includes 5 tables – “Regions”, “Vulnerability”,
“Susceptibility”, “Coping capacity” and “Adaptive capacity” (Fig 3). Each table contains ID numbers, values of
subcomponents and their respective weights, which have been used for calculation of vulnerability indicators and index
for each region of RA. The table “Regions” contains name, area and population data for each region of RA.
The tables “Susceptibility”, “Coping capacity” and “Adaptive capacity” are connected to the table “Vulnerability”,
which is connected to the table “Regions”. All tables are connected to appropriate digital layer of a geo-database
included in the ERRA.
Based on the calculated values, 4 separate thematic maps representing vulnerability indicators (Susceptibility, Coping
capacity and Adaptive capacity) and vulnerability index at regional level have been developed. “Susceptibility”,
“Coping capacity” and “Adaptive capacity” maps show spatial distribution of vulnerability factors separately, while
“Vulnerability” map shows distribution of overall vulnerability index in the territory of Armenia.
Spatial analysis of susceptibility, coping capacity, adaptive capacity indicators and the vulnerability index shows the
trend of population vulnerability increase from the central part of the country to the north and south. This shows
insufficient capability of population to anticipate, cope with, resist, respond and rehabilitate from the impact of hazards
in the regions located far from the capital Yerevan.
The population of Yerevan and central regions of RA are relatively resistant to the impact of natural and technological
hazards.
Fig. 3. Vulnerability database structure and content.
This approach of assessment and mapping of population vulnerability has been tested at local level (community level)
for 10 communities of Tavush region. The structure of database and its relation to the thematic maps allows developing
a set of vulnerability maps at local level for RA and including them in the ERRA.
Proceedings, 6th
International Conference on Cartography and GIS, 13-17 June 2016, Albena, Bulgaria
ISSN: 1314-0604, Eds: Bandrova T., Konecny M.
845
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