Earthquake vulnerability assessment for the Indian subcontinent using the Long Short-Term Memory model (LSTM)

Jena, R; Naik, SP; Pradhan, B; Beydoun, G; Park, HJ; Alamri, A

Earthquake vulnerability assessment for the Indian subcontinent using the Long Short-Term Memory model (LSTM)

Jena, R

Naik, SP Pradhan, B

Beydoun, G

Park, HJ Alamri, A

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: International Journal of Disaster Risk Reduction, 2021, 66, pp. 1-14
Issue Date:: 2021-12-01

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Field	Value	Language
dc.contributor.author	Jena, R https://orcid.org/0000-0002-6180-4673
dc.contributor.author	Naik, SP
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Beydoun, G https://orcid.org/0000-0001-8087-5445
dc.contributor.author	Park, HJ
dc.contributor.author	Alamri, A
dc.date.accessioned	2022-02-18T02:35:57Z
dc.date.available	2022-02-18T02:35:57Z
dc.date.issued	2021-12-01
dc.identifier.citation	International Journal of Disaster Risk Reduction, 2021, 66, pp. 1-14
dc.identifier.issn	2212-4209
dc.identifier.issn	2212-4209
dc.identifier.uri	http://hdl.handle.net/10453/154676
dc.description.abstract	Earthquakes are one of the most destructive and unpredictable natural hazards with a long-term physical, psychological, and economic impact to the society. In the past century, more than 1100 destructive earthquakes occurred, and caused around 1.5 million deaths worldwide. Some recent studies have suggested that a future earthquake in the Himalayan region of magnitude range MW 7.5–8 can cause more than 0.2 million human lives and around 150 billion dollar financial loss. Deep learning methods in recent studies proved very useful in natural hazards forecasting and prediction modelling. Long Short-Term Memory (LSTM) model has been particularly popular in several natural hazard forecasting. In this research, for the first time, LSTM model is implemented with suitable Geospatial Information Systems (GIS) techniques to assess the earthquake vulnerability for whole of India. In India, most of the seismic vulnerability assessment available are at city level or state level using traditional techniques. Several factors such as land use, geology, geomorphology, fault distribution, transportation facility, population density were all used to develop the social, structural, and geotechnical vulnerability maps. The results show that the areas around Delhi, NE region of India, major parts of Gujrat, West Bengal plain exhibit high to very-high seismic vulnerability. This model achieved an accuracy of 87.8%, sensitivity (90%) and specificity (84.9%). The present analysis can be helpful towards prioritization of regions which are in higher need of risk reduction interventions. Also, based on this vulnerability index map, the risk metrics can be attenuated.
dc.language	English
dc.publisher	Elsevier
dc.relation	Rich Data Corporation (Australia) Pty Ltd
dc.relation.ispartof	International Journal of Disaster Risk Reduction
dc.relation.isbasedon	10.1016/j.ijdrr.2021.102642
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0502 Environmental Science and Management, 1117 Public Health and Health Services, 1604 Human Geography
dc.title	Earthquake vulnerability assessment for the Indian subcontinent using the Long Short-Term Memory model (LSTM)
dc.type	Journal Article
utslib.citation.volume	66
utslib.for	0502 Environmental Science and Management
utslib.for	1117 Public Health and Health Services
utslib.for	1604 Human Geography
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/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-02-18T02:35:49Z
pubs.publication-status	Published
pubs.volume	66

Abstract:

Earthquakes are one of the most destructive and unpredictable natural hazards with a long-term physical, psychological, and economic impact to the society. In the past century, more than 1100 destructive earthquakes occurred, and caused around 1.5 million deaths worldwide. Some recent studies have suggested that a future earthquake in the Himalayan region of magnitude range MW 7.5–8 can cause more than 0.2 million human lives and around 150 billion dollar financial loss. Deep learning methods in recent studies proved very useful in natural hazards forecasting and prediction modelling. Long Short-Term Memory (LSTM) model has been particularly popular in several natural hazard forecasting. In this research, for the first time, LSTM model is implemented with suitable Geospatial Information Systems (GIS) techniques to assess the earthquake vulnerability for whole of India. In India, most of the seismic vulnerability assessment available are at city level or state level using traditional techniques. Several factors such as land use, geology, geomorphology, fault distribution, transportation facility, population density were all used to develop the social, structural, and geotechnical vulnerability maps. The results show that the areas around Delhi, NE region of India, major parts of Gujrat, West Bengal plain exhibit high to very-high seismic vulnerability. This model achieved an accuracy of 87.8%, sensitivity (90%) and specificity (84.9%). The present analysis can be helpful towards prioritization of regions which are in higher need of risk reduction interventions. Also, based on this vulnerability index map, the risk metrics can be attenuated.

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http://hdl.handle.net/10453/154676