Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model

Pradhan, B; Lee, S; Dikshit, A; Kim, H

Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model

Pradhan, B

Lee, S Dikshit, A

Kim, H

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Geoscience Frontiers, 2023, 14, (6), pp. 101625
Issue Date:: 2023-11-01

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Field	Value	Language
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Lee, S
dc.contributor.author	Dikshit, A https://orcid.org/0000-0003-2876-4080
dc.contributor.author	Kim, H
dc.date.accessioned	2024-04-12T01:51:56Z
dc.date.available	2024-04-12T01:51:56Z
dc.date.issued	2023-11-01
dc.identifier.citation	Geoscience Frontiers, 2023, 14, (6), pp. 101625
dc.identifier.issn	1674-9871
dc.identifier.uri	http://hdl.handle.net/10453/177805
dc.description.abstract	Floods are natural hazards that lead to devastating financial losses and large displacements of people. Flood susceptibility maps can improve mitigation measures according to the specific conditions of a study area. The design of flood susceptibility maps has been enhanced through use of hybrid machine learning and deep learning models. Although these models have achieved better accuracy than traditional models, they are not widely used by stakeholders due to their black-box nature. In this study, we propose the application of an explainable artificial intelligence (XAI) model that incorporates the Shapley additive explanation (SHAP) model to interpret the outcomes of convolutional neural network (CNN) deep learning models, and analyze the impact of variables on flood susceptibility mapping. This study was conducted in Jinju Province, South Korea, which has a long history of flood events. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), which showed a prediction accuracy of 88.4%. SHAP plots showed that land use and various soil attributes significantly affected flood susceptibility in the study area. In light of these findings, we recommend the use of XAI-based models in future flood susceptibility mapping studies to improve interpretations of model outcomes, and build trust among stakeholders during the flood-related decision-making process.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Geoscience Frontiers
dc.relation.isbasedon	10.1016/j.gsf.2023.101625
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0402 Geochemistry, 0403 Geology, 0404 Geophysics
dc.subject.classification	3703 Geochemistry
dc.subject.classification	3705 Geology
dc.subject.classification	3709 Physical geography and environmental geoscience
dc.title	Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model
dc.type	Journal Article
utslib.citation.volume	14
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
utslib.copyright.status	open_access	*
dc.date.updated	2024-04-12T01:51:54Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	6

Abstract:

Floods are natural hazards that lead to devastating financial losses and large displacements of people. Flood susceptibility maps can improve mitigation measures according to the specific conditions of a study area. The design of flood susceptibility maps has been enhanced through use of hybrid machine learning and deep learning models. Although these models have achieved better accuracy than traditional models, they are not widely used by stakeholders due to their black-box nature. In this study, we propose the application of an explainable artificial intelligence (XAI) model that incorporates the Shapley additive explanation (SHAP) model to interpret the outcomes of convolutional neural network (CNN) deep learning models, and analyze the impact of variables on flood susceptibility mapping. This study was conducted in Jinju Province, South Korea, which has a long history of flood events. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), which showed a prediction accuracy of 88.4%. SHAP plots showed that land use and various soil attributes significantly affected flood susceptibility in the study area. In light of these findings, we recommend the use of XAI-based models in future flood susceptibility mapping studies to improve interpretations of model outcomes, and build trust among stakeholders during the flood-related decision-making process.

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http://hdl.handle.net/10453/177805