Earthquake hazard and risk assessment using machine learning approaches at Palu, Indonesia

Jena, R; Pradhan, B; Beydoun, G; Alamri, AM; Ardiansyah; Nizamuddin; Sofyan, H

Earthquake hazard and risk assessment using machine learning approaches at Palu, Indonesia

Jena, R

Pradhan, B

Beydoun, G

Alamri, AM Ardiansyah Nizamuddin Sofyan, H

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Science of the Total Environment, 2020, 749, pp. 141582-141582
Issue Date:: 2020-12-20

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Field	Value	Language
dc.contributor.author	Jena, R https://orcid.org/0000-0002-6180-4673
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	Alamri, AM
dc.contributor.author	Ardiansyah
dc.contributor.author	Nizamuddin
dc.contributor.author	Sofyan, H
dc.date.accessioned	2020-12-20T05:51:01Z
dc.date.available	2020-12-20T05:51:01Z
dc.date.issued	2020-12-20
dc.identifier.citation	Science of the Total Environment, 2020, 749, pp. 141582-141582
dc.identifier.issn	0048-9697
dc.identifier.issn	1879-1026
dc.identifier.uri	http://hdl.handle.net/10453/144873
dc.description.abstract	© 2020 Elsevier B.V. On 28th September 2018, a very high magnitude of earthquake Mw 7.5 struck the Palu city in the Island of Sulawesi, Indonesia. The main objective of this research is to estimate the earthquake risk based on probability and hazard in Palu region using cross-correlation among the derived parameters, Silhouette clustering (SC), pure locational clustering (PLC) based on hierarchical clustering analysis (HCA), convolutional neural network (CNN) and analytical hierarchy process (AHP) techniques. There is no specific or simple way of identifying risks as the definition of risk varies with time and space. The main aim of this study is: i) to conduct the clustering analysis to identify the earthquake-prone areas, ii) to develop a CNN model for probability estimation, and iii) to estimate and compare the risk using two calculation equations (Risk A and B). Owing to its high prediction ability, the CNN model assessed the probability while SC and PLC were implemented to understand the spatial clustering, Euclidean distance among clusters, spatial relationship and cross-correlation among the estimated Mw, PGA and intensity including events depth. Finally, AHP was implemented for the vulnerability assessment. To this end, earthquake probability assessment (EPA), susceptibility to seismic amplification (SSA) and earthquake vulnerability assessment (EVA) results were employed to generate risk A, while earthquake hazard assessment (EHA), SSA and EVA were used to generate risk B. The risk maps were compared and the differences in results were obtained. This research concludes that in the case of earthquake risk assessment (ERA), results obtained in Risk B are better than the risk A. This study achieved 89.47% accuracy for EPA while for EVA a consistency ratio of 0.07. These results have important implications for future large-scale risk assessment, land use planning and hazard mitigation.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Science of the Total Environment
dc.relation.isbasedon	10.1016/j.scitotenv.2020.141582
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Environmental Sciences
dc.title	Earthquake hazard and risk assessment using machine learning approaches at Palu, Indonesia
dc.type	Journal Article
utslib.citation.volume	749
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
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
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
utslib.copyright.status	closed_access	*
dc.date.updated	2020-12-20T05:50:54Z
pubs.publication-status	Accepted
pubs.volume	749

Abstract:

© 2020 Elsevier B.V. On 28th September 2018, a very high magnitude of earthquake Mw 7.5 struck the Palu city in the Island of Sulawesi, Indonesia. The main objective of this research is to estimate the earthquake risk based on probability and hazard in Palu region using cross-correlation among the derived parameters, Silhouette clustering (SC), pure locational clustering (PLC) based on hierarchical clustering analysis (HCA), convolutional neural network (CNN) and analytical hierarchy process (AHP) techniques. There is no specific or simple way of identifying risks as the definition of risk varies with time and space. The main aim of this study is: i) to conduct the clustering analysis to identify the earthquake-prone areas, ii) to develop a CNN model for probability estimation, and iii) to estimate and compare the risk using two calculation equations (Risk A and B). Owing to its high prediction ability, the CNN model assessed the probability while SC and PLC were implemented to understand the spatial clustering, Euclidean distance among clusters, spatial relationship and cross-correlation among the estimated Mw, PGA and intensity including events depth. Finally, AHP was implemented for the vulnerability assessment. To this end, earthquake probability assessment (EPA), susceptibility to seismic amplification (SSA) and earthquake vulnerability assessment (EVA) results were employed to generate risk A, while earthquake hazard assessment (EHA), SSA and EVA were used to generate risk B. The risk maps were compared and the differences in results were obtained. This research concludes that in the case of earthquake risk assessment (ERA), results obtained in Risk B are better than the risk A. This study achieved 89.47% accuracy for EPA while for EVA a consistency ratio of 0.07. These results have important implications for future large-scale risk assessment, land use planning and hazard mitigation.

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http://hdl.handle.net/10453/144873