Abstract
Vulnerability assessment is one of the prerequisites for risk analysis in disaster management. Vulnerability to earthquakes, especially in urban areas, has increased over the years due to the presence of complex urban structures and rapid development. Urban vulnerability is a result of human behavior which describes the extent of susceptibility or resilience of social, economic, and physical assets to natural disasters. The main aim of this paper is to develop a new hybrid framework using Analytic Network Process (ANP) and Artificial Neural Network (ANN) models for constructing a composite social, economic, environmental, and physical vulnerability index. This index was then applied to Tabriz City, which is a seismic-prone province in the northwestern part of Iran with recurring devastating earthquakes and consequent heavy casualties and damages. A Geographical Information Systems (GIS) analysis was used to identify and evaluate quantitative vulnerability indicators for generating an earthquake vulnerability map. The classified and standardized indicators were subsequently weighed and ranked using an ANP model to construct the training database. Then, standardized maps coupled with the training site maps were presented as input to a Multilayer Perceptron (MLP) neural network for producing an Earthquake Vulnerability Map (EVM). Finally, an EVM was produced for Tabriz City and the level of vulnerability in various zones was obtained. South and southeast regions of Tabriz City indicate low to moderate vulnerability, while some zones of the northeastern tract are under critical vulnerability conditions. Furthermore, the impact of the vulnerability of Tabriz City on population during an earthquake was included in this analysis for risk estimation. A comparison of the result produced by EVM and the Population Vulnerability (PV) of Tabriz City corroborated the validity of the results obtained by ANP-ANN. The findings of this paper are useful for decision-makers and government authorities to obtain a better knowledge of a city’s vulnerability dimensions, and to adopt preparedness strategies in the future for Tabriz City. The developed hybrid framework of ANP and ANN Models can easily be replicated and applied to other urban regions around the world for sustainability and environmental management.
Highlights
Research studies on worldwide change of environment and sustainability sciences have found vulnerability as the main concern [1,2,3,4,5,6,7,8,9,10]
The main objectives of this paper are: (1) to develop a hybrid Analytic Network Process (ANP)-Artificial Neural Network (ANN) model in geographic information system (GIS); (2) to assess four main dimensions of earthquake vulnerability such as social, economic, environmental, and physical an Earthquake Vulnerability Map (EVM) for Tabriz City; and (3) to compare the results with data provided by Population Vulnerability (PV) with an aim to reduce the impact of an earthquake by determining and categorizing the most vulnerable zones
The outline of the hybrid ANP-ANN model developed in this contribution is flexible, as it is an integrated approach to earthquake vulnerability assessment
Summary
Research studies on worldwide change of environment and sustainability sciences have found vulnerability as the main concern [1,2,3,4,5,6,7,8,9,10]. Earthquake vulnerability assessment methods generally focus on magnitude prediction [19,20,21,22,23,24,25,26,27,28] and structural and geological engineering aspects [29,30,31,32,33,34,35,36] Apart from their devastating effects on built-up areas, earthquakes can have significant impacts on economic degradation, social structure, and the cultural heritage of an urban area. To effectively utilize a strong evaluation method for an earthquake vulnerability assessment, it is necessary to incorporate some important vulnerability components of an urban area, such as socio-economic, environmental and physical components
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