Assessing Coastal Flood Susceptibility in East Java, Indonesia: Comparison of Statistical Bivariate and Machine Learning Techniques

Hidayah, E; Indarto,; Lee, WK; Halik, G; Pradhan, B

Assessing Coastal Flood Susceptibility in East Java, Indonesia: Comparison of Statistical Bivariate and Machine Learning Techniques

Hidayah, E Indarto, Lee, WK Halik, G Pradhan, B

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Water (Switzerland), 2022, 14, (23), pp. 3869
Issue Date:: 2022-12-01

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Field	Value	Language
dc.contributor.author	Hidayah, E
dc.contributor.author	Indarto,
dc.contributor.author	Lee, WK
dc.contributor.author	Halik, G
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.date.accessioned	2023-03-20T03:03:27Z
dc.date.available	2023-03-20T03:03:27Z
dc.date.issued	2022-12-01
dc.identifier.citation	Water (Switzerland), 2022, 14, (23), pp. 3869
dc.identifier.issn	2073-4441
dc.identifier.issn	2073-4441
dc.identifier.uri	http://hdl.handle.net/10453/167723
dc.description.abstract	Floods in coastal areas occur yearly in Indonesia, resulting in socio-economic losses. The availability of flood susceptibility maps is essential for flood mitigation. This study aimed to explore four different types of models, namely, frequency ratio (FR), weight of evidence (WofE), random forest (RF), and multi-layer perceptron (MLP), for coastal flood susceptibility assessment in Pasuruan and Probolinggo in the East Java region. Factors were selected based on multi-collinearity and the information gain ratio to build flood susceptibility maps in small watersheds. The comprehensive exploration result showed that seven of the eleven factors, namely, elevation, geology, soil type, land use, rainfall, RD, and TWI, influenced the coastal flood susceptibility. The MLP outperformed the other three models, with an accuracy of 0.977. Assessing flood susceptibility with those four methods can guide flood mitigation management.
dc.language	en
dc.publisher	MDPI
dc.relation.ispartof	Water (Switzerland)
dc.relation.isbasedon	10.3390/w14233869
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Assessing Coastal Flood Susceptibility in East Java, Indonesia: Comparison of Statistical Bivariate and Machine Learning Techniques
dc.type	Journal Article
utslib.citation.volume	14
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	2023-03-20T03:03:25Z
pubs.issue	23
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	23

Abstract:

Floods in coastal areas occur yearly in Indonesia, resulting in socio-economic losses. The availability of flood susceptibility maps is essential for flood mitigation. This study aimed to explore four different types of models, namely, frequency ratio (FR), weight of evidence (WofE), random forest (RF), and multi-layer perceptron (MLP), for coastal flood susceptibility assessment in Pasuruan and Probolinggo in the East Java region. Factors were selected based on multi-collinearity and the information gain ratio to build flood susceptibility maps in small watersheds. The comprehensive exploration result showed that seven of the eleven factors, namely, elevation, geology, soil type, land use, rainfall, RD, and TWI, influenced the coastal flood susceptibility. The MLP outperformed the other three models, with an accuracy of 0.977. Assessing flood susceptibility with those four methods can guide flood mitigation management.

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http://hdl.handle.net/10453/167723