A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment

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.

Flood Susceptibility Mapping Using GIS-Based Analytic Network Process: A Case Study of Perlis, Malaysia

“…There is a dearth of published research that combined GIS-based ANP model and RS to forecast flood susceptible zones in Perlis, Malaysia. Recent studies that adopted the ANP method have not incorporated remote sensing microwave images such as Radar Satellite (RADARSAT) into the analysis [10][11][12]. Moreover, Perlis is in the northern part of Malaysia that is regarded as the "rice bowl" of the country [13].…”

Achieving Neuroplasticity in Artificial Neural Networks through Smart Cities

“…Lee, et al [45] made use of ANNs to predict mosquito abundances in urban areas. Using the vast applicability of ANNs, some urban fabric models such as CORINA [43], ART-MMAP [46], the ANP-ANN model for urban earthquake vulnerability assessment [47], and cellular automata (CA) [23] were developed and applied for diverse purposes.…”

Application of Association Rules to Determine Building Typological Classes for Seismic Damage Predictions at Regional Scale: The Case Study of Basel

“…Over the last few years, surveys involving drones, satellites, and open maps have been introduced to reduce the time demand (Suzuki et al, 2010;Ehrlich et al, 2013). Deep-learning approaches, collecting data from pictures of buildings or other sources are then implemented for automatic attribution of types (Mallepudi et al, 2011) or directly for seismic-vulnerability estimates (Alizadeh et al, 2018).…”