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A GIS Based Earthquake Hazard Pattern Identification Implementing the Local Site-Specific Parameters and the Historical Seismicity
1 Geoinformatics Department, Netaji Subhas University of Technology, Dwarka, New Delhi, 110078, India
2 Geographic Information System Area, NIIT University, Neemrana, Alwar, 301705, India
3 Geosciences & Geohazard Department, Indian Institute of Remote Sensing (IIRS), Dehradun, 248001, India
4 Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India
5 Department of Geophysics, Kurukshetra University, Kurukshetra, 136119, India
* Corresponding Author: Abhishek Rawat. Email:
Revue Internationale de Géomatique 2025, 34, 351-362. https://doi.org/10.32604/rig.2025.064031
Received 02 February 2025; Accepted 09 June 2025; Issue published 30 June 2025
Abstract
The unconsolidated soils of the Indo-Gangetic Plains (IGP) contribute significantly to the amplification of seismic damage during earthquakes. Site-specific effects play a critical role in intensifying ground motion and shaping the spatial distribution of seismic hazards. This study aims to investigate the spatial variability of seismic hazards using geophysical and geological parameters such as lithology, shear wave velocity, soil texture, basement depth, and proximity to fault lines. Training data were derived from common hazard points identified in earthquake catalogues. Several machine learning (ML) models, including Logistic Regression (LR), K-Nearest Neighbors, Random Forest, and Decision Tree, were employed to analyze the variability of seismic hazards in North Bihar. These models achieved classification accuracies of 65%, 67%, 87%, and 77%, respectively, in identifying hazard patterns. The generalized hazard map generated using the Random Forest algorithm can serve as a valuable tool for estimating the extent of seismic risk when integrated with ground motion parameters following an earthquake.Keywords
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Copyright © 2025 The Author(s). Published by Tech Science Press.This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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