Mapping seismic risk in Rangpur Division, Bangladesh: an integrated geospatial approach

Hasan, M; Hoque, MAA; Pradhan, B

Mapping seismic risk in Rangpur Division, Bangladesh: an integrated geospatial approach

Hasan, M Hoque, MAA Pradhan, B

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Publisher:: SPRINGER SINGAPORE PTE LTD
Publication Type:: Journal Article
Citation:: Spatial Information Research, 2025, 33, (5)
Issue Date:: 2025-10-01

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Field	Value	Language
dc.contributor.author	Hasan, M
dc.contributor.author	Hoque, MAA
dc.contributor.author	Pradhan, B
dc.date.accessioned	2025-09-03T07:56:54Z
dc.date.available	2025-09-03T07:56:54Z
dc.date.issued	2025-10-01
dc.identifier.citation	Spatial Information Research, 2025, 33, (5)
dc.identifier.issn	2366-3286
dc.identifier.issn	2366-3294
dc.identifier.uri	http://hdl.handle.net/10453/189620
dc.description.abstract	Rangpur Division in Bangladesh lies near the active Dauki Fault, yet few studies have fully assessed its earthquake risk. Most research focuses separately on hazard or vulnerability, often ignoring mitigation and detailed spatial analysis. This study fills that gap by using a geospatial multi-criteria approach that integrates hazard, vulnerability, and mitigation indicators into one risk assessment model. Employing Geographic Information Systems (GIS), Analytic Hierarchy Process (AHP), and Multi-Criteria Decision Analysis (MCDA) in Python, nine hazard criteria, seven vulnerability indicators, and five mitigation factors were modeled as weighted raster layers. These layers were combined to produce high-resolution maps and a seismic risk map with five risk levels from very high to very low. Model validation showed strong predictive ability (AUC = 0.857) and consistency (CR < 0.1). Results reveal that 21.43% of Rangpur is at very high risk, mainly due to proximity to fault lines and dense population, while only 10.62% shows strong mitigation capacity. This highlights the urgent need for better disaster preparedness, infrastructure improvement, and public awareness. The spatial outputs provide valuable guidance for policymakers on urban planning, risk reduction, and resilience-building in the region.
dc.language	English
dc.publisher	SPRINGER SINGAPORE PTE LTD
dc.relation.ispartof	Spatial Information Research
dc.relation.isbasedon	10.1007/s41324-025-00643-y
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	4601 Applied computing
dc.subject.classification	4905 Statistics
dc.title	Mapping seismic risk in Rangpur Division, Bangladesh: an integrated geospatial approach
dc.type	Journal Article
utslib.citation.volume	33
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS)/Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS) Associate Members
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-09-03T07:56:39Z
pubs.issue	5
pubs.publication-status	Accepted
pubs.volume	33
utslib.citation.issue	5

Abstract:

Rangpur Division in Bangladesh lies near the active Dauki Fault, yet few studies have fully assessed its earthquake risk. Most research focuses separately on hazard or vulnerability, often ignoring mitigation and detailed spatial analysis. This study fills that gap by using a geospatial multi-criteria approach that integrates hazard, vulnerability, and mitigation indicators into one risk assessment model. Employing Geographic Information Systems (GIS), Analytic Hierarchy Process (AHP), and Multi-Criteria Decision Analysis (MCDA) in Python, nine hazard criteria, seven vulnerability indicators, and five mitigation factors were modeled as weighted raster layers. These layers were combined to produce high-resolution maps and a seismic risk map with five risk levels from very high to very low. Model validation showed strong predictive ability (AUC = 0.857) and consistency (CR < 0.1). Results reveal that 21.43% of Rangpur is at very high risk, mainly due to proximity to fault lines and dense population, while only 10.62% shows strong mitigation capacity. This highlights the urgent need for better disaster preparedness, infrastructure improvement, and public awareness. The spatial outputs provide valuable guidance for policymakers on urban planning, risk reduction, and resilience-building in the region.

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http://hdl.handle.net/10453/189620