Volume 11 Issue 2
Aug.  2020
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Ratiranjan Jena, Biswajeet Pradhan, Ghassan Beydoun, Nizamuddin, Ardiansyah, Hizir Sofyan, Muzailin Affan. Integrated model for earthquake risk assessment using neural network andanalytic hierarchy process: Aceh province, Indonesia[J]. Geoscience Frontiers, 2020, (2): 613-634. doi: 10.1016/j.gsf.2019.07.006
Citation: Ratiranjan Jena, Biswajeet Pradhan, Ghassan Beydoun, Nizamuddin, Ardiansyah, Hizir Sofyan, Muzailin Affan. Integrated model for earthquake risk assessment using neural network and analytic hierarchy process: Aceh province, Indonesia[J]. Geoscience Frontiers, 2020, (2): 613-634. doi: 10.1016/j.gsf.2019.07.006

Integrated model for earthquake risk assessment using neural network and analytic hierarchy process: Aceh province, Indonesia

doi: 10.1016/j.gsf.2019.07.006
Funds:

This research is funded by Centre for Advanced Modelling and Geospatial Information Systems, University of Technology Sydney: 323930, 321740.2232335 and 321740.2232357.

  • Received Date: 2019-04-03
  • Rev Recd Date: 2019-05-21
  • Publish Date: 2020-08-26
  • 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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