A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods

Khosravi, K; Shahabi, H; Pham, BT; Adamowski, J; Shirzadi, A; Pradhan, B; Dou, J; Ly, HB; Gróf, G; Ho, HL; Hong, H; Chapi, K; Prakash, I

A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods

Khosravi, K Shahabi, H Pham, BT Adamowski, J Shirzadi, A Pradhan, B

Dou, J Ly, HB Gróf, G Ho, HL Hong, H Chapi, K Prakash, I

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Publication Type:: Journal Article
Citation:: Journal of Hydrology, 2019, 573 pp. 311 - 323
Issue Date:: 2019-06-01

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Field	Value	Language
dc.contributor.author	Khosravi, K	en_US
dc.contributor.author	Shahabi, H	en_US
dc.contributor.author	Pham, BT	en_US
dc.contributor.author	Adamowski, J	en_US
dc.contributor.author	Shirzadi, A	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.contributor.author	Dou, J	en_US
dc.contributor.author	Ly, HB	en_US
dc.contributor.author	Gróf, G	en_US
dc.contributor.author	Ho, HL	en_US
dc.contributor.author	Hong, H	en_US
dc.contributor.author	Chapi, K	en_US
dc.contributor.author	Prakash, I	en_US
dc.date.issued	2019-06-01	en_US
dc.identifier.citation	Journal of Hydrology, 2019, 573 pp. 311 - 323	en_US
dc.identifier.issn	0022-1694	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134742
dc.description.abstract	© 2019 Elsevier B.V. Floods around the world are having devastating effects on human life and property. In this paper, three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS and SAW), along with two machine learning methods (NBT and NB), were tested for their ability to model flood susceptibility in one of China's most flood-prone areas, the Ningdu Catchment. Twelve flood conditioning factors were used as input parameters: Normalized Difference Vegetation Index (NDVI), lithology, land use, distance from river, curvature, altitude, Stream Transport Index (STI), Topographic Wetness Index (TWI), Stream Power Index (SPI), soil type, slope and rainfall. The predictive capacity of the models was evaluated and validated using the Area Under the Receiver Operating Characteristic curve (AUC). While all models showed a strong flood prediction capability (AUC > 0.95), the NBT model performed best (AUC = 0.98), suggesting that, among the models studied, the NBT model is a promising tool for the assessment of flood-prone areas and can allow for proper planning and management of flood hazards.	en_US
dc.relation.ispartof	Journal of Hydrology	en_US
dc.relation.isbasedon	10.1016/j.jhydrol.2019.03.073	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Environmental Engineering	en_US
dc.title	A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods	en_US
dc.type	Journal Article
utslib.citation.volume	573	en_US
utslib.for	MD Multidisciplinary	en_US
pubs.embargo.period	Not known	en_US
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 Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	573	en_US

Abstract:

© 2019 Elsevier B.V. Floods around the world are having devastating effects on human life and property. In this paper, three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS and SAW), along with two machine learning methods (NBT and NB), were tested for their ability to model flood susceptibility in one of China's most flood-prone areas, the Ningdu Catchment. Twelve flood conditioning factors were used as input parameters: Normalized Difference Vegetation Index (NDVI), lithology, land use, distance from river, curvature, altitude, Stream Transport Index (STI), Topographic Wetness Index (TWI), Stream Power Index (SPI), soil type, slope and rainfall. The predictive capacity of the models was evaluated and validated using the Area Under the Receiver Operating Characteristic curve (AUC). While all models showed a strong flood prediction capability (AUC > 0.95), the NBT model performed best (AUC = 0.98), suggesting that, among the models studied, the NBT model is a promising tool for the assessment of flood-prone areas and can allow for proper planning and management of flood hazards.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/134742