Spatial earthquake vulnerability assessment by using multi-criteria decision making and probabilistic neural network techniques in Odisha, India

Abstract

In this study, the multi-criteria decision-making method was used to estimate the weights of several input factors such as slope, curvature, elevation, proximity to road, road density, proximity to land use, land use density, proximity to water bodies, river density, rail density, distance from rail, groundwater variation, lithology with amplification factors, peak ground acceleration (PGA) variation, and population density. An integrated analytic hierarchy process (AHP) and a probabilistic neural network (PNN) were applied for the Earthquake vulnerability assessment (EVA). The PNN model successfully explored the relationship between variables and weights obtained from the AHP approach. Validation results indicate that 92.5% accuracy was attained by the PNN model. According to the results, 24.26%, 15.26%, and 20.58% of the area fall under very-high, high, and moderate vulnerability category, respectively. The EVA map illustrates that high to very-high impact could be observed in coastal Odisha and few districts in the Mahanadi Graven.

Keywords:

• Earthquake vulnerability
• MCDM
• Bayesian classifier
• probabilistic neural network
• GIS

Data availability

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering & IT, University of Technology Sydney (UTS), in part by the Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia, under Grant RSP-2021/14.