A new method to promptly evaluate spatial earthquake probability mapping using an explainable artificial intelligence (XAI) model

Jena, R; Pradhan, B; Gite, S; Alamri, A; Park, HJ

A new method to promptly evaluate spatial earthquake probability mapping using an explainable artificial intelligence (XAI) model

Jena, R

Pradhan, B

Gite, S Alamri, A Park, HJ

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Gondwana Research, 2022
Issue Date:: 2022-01-01

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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	Gite, S
dc.contributor.author	Alamri, A
dc.contributor.author	Park, HJ
dc.date.accessioned	2023-04-26T04:24:46Z
dc.date.available	2023-04-26T04:24:46Z
dc.date.issued	2022-01-01
dc.identifier.citation	Gondwana Research, 2022
dc.identifier.issn	1342-937X
dc.identifier.uri	http://hdl.handle.net/10453/170111
dc.description.abstract	Machine learning (ML) models have been extensively used in several geological applications. Owing to the increase in model complexity, interpreting the outputs becomes quite challenging. Shapley additive explanation (SHAP) measures the importance of each input attribute on the model's output. This study implemented SHAP to estimate earthquake probability using two different types of ML approaches, namely, artificial neural network (ANN) and random forest (RF). The two algorithms were first compared to evaluate the importance and effect of the factors. SHAP was then carried out to interpret the output of the models designed for the earthquake probability. This study aims not only to achieve high accuracy in probability estimation but also to rank the input parameters and select appropriate features for classification. SHAP was tested on earthquake probability assessment using eight factors for the Indian subcontinent. The models obtained an overall accuracy of 96 % for ANN and 98 % for RF. SHAP identified the high contributing factors as epicenter distance, depth density, intensity variation, and magnitude density in a sequential order for ANN. Finally, the authors argued that an explainable artificial intelligence (AI) model can help in earthquake probability estimation, which then open avenues to building a transferable AI model.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Gondwana Research
dc.relation.isbasedon	10.1016/j.gr.2022.10.003
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0402 Geochemistry, 0403 Geology, 0404 Geophysics
dc.subject.classification	Geology
dc.title	A new method to promptly evaluate spatial earthquake probability mapping using an explainable artificial intelligence (XAI) model
dc.type	Journal Article
utslib.for	0402 Geochemistry
utslib.for	0403 Geology
utslib.for	0404 Geophysics
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/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-26T04:24:44Z
pubs.publication-status	Published

Abstract:

Machine learning (ML) models have been extensively used in several geological applications. Owing to the increase in model complexity, interpreting the outputs becomes quite challenging. Shapley additive explanation (SHAP) measures the importance of each input attribute on the model's output. This study implemented SHAP to estimate earthquake probability using two different types of ML approaches, namely, artificial neural network (ANN) and random forest (RF). The two algorithms were first compared to evaluate the importance and effect of the factors. SHAP was then carried out to interpret the output of the models designed for the earthquake probability. This study aims not only to achieve high accuracy in probability estimation but also to rank the input parameters and select appropriate features for classification. SHAP was tested on earthquake probability assessment using eight factors for the Indian subcontinent. The models obtained an overall accuracy of 96 % for ANN and 98 % for RF. SHAP identified the high contributing factors as epicenter distance, depth density, intensity variation, and magnitude density in a sequential order for ANN. Finally, the authors argued that an explainable artificial intelligence (AI) model can help in earthquake probability estimation, which then open avenues to building a transferable AI model.

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http://hdl.handle.net/10453/170111