Integrated model for earthquake risk assessment using neural network and
analytic hierarchy process: Aceh province, Indonesia
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Abstract
Catastrophic natural hazards, such as earthquake, pose serious threats to properties and human lives in urban
areas. Therefore, earthquake risk assessment (ERA) is indispensable in disaster management. ERA is an integration
of the extent of probability and vulnerability of assets. This study develops an integrated model by using
the artificial neural network–analytic hierarchy process (ANN–AHP) model for constructing the ERA map. The
aim of the study is to quantify urban population risk that may be caused by impending earthquakes. The model is
applied to the city of Banda Aceh in Indonesia, a seismically active zone of Aceh province frequently affected by
devastating earthquakes. ANN is used for probability mapping, whereas AHP is used to assess urban vulnerability
after the hazard map is created with the aid of earthquake intensity variation thematic layering. The risk map is
subsequently created by combining the probability, hazard, and vulnerability maps. Then, the risk levels of
various zones are obtained. The validation process reveals that the proposed model can map the earthquake
probability based on historical events with an accuracy of 84%. Furthermore, results show that the central and
southeastern regions of the city have moderate to very high risk classifications, whereas the other parts of the city
fall under low to very low earthquake risk classifications. The findings of this research are useful for government
agencies and decision makers, particularly in estimating risk dimensions in urban areas and for the future studies
to project the preparedness strategies for Banda Aceh.
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