Assessment of data mining, multi-criteria decision making and fuzzy-computing techniques for spatial flood susceptibility mapping: a comparative study

Balogun, AL; Sheng, TY; Sallehuddin, MH; Aina, YA; Dano, UL; Pradhan, B; Yekeen, S; Tella, A

Assessment of data mining, multi-criteria decision making and fuzzy-computing techniques for spatial flood susceptibility mapping: a comparative study

Balogun, AL Sheng, TY Sallehuddin, MH Aina, YA Dano, UL Pradhan, B

Yekeen, S Tella, A

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Publisher:: TAYLOR & FRANCIS LTD
Publication Type:: Journal Article
Citation:: Geocarto International, 2022, 37, (26), pp. 12989-13015
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Balogun, AL
dc.contributor.author	Sheng, TY
dc.contributor.author	Sallehuddin, MH
dc.contributor.author	Aina, YA
dc.contributor.author	Dano, UL
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Yekeen, S
dc.contributor.author	Tella, A
dc.date.accessioned	2023-04-03T01:53:33Z
dc.date.available	2023-04-03T01:53:33Z
dc.date.issued	2022-01-01
dc.identifier.citation	Geocarto International, 2022, 37, (26), pp. 12989-13015
dc.identifier.issn	1010-6049
dc.identifier.issn	1752-0762
dc.identifier.uri	http://hdl.handle.net/10453/169053
dc.description.abstract	This study develops an Adaboost-GIS model for flood susceptibility mapping and evaluates its relative performance by undertaking a comparative assessment of the machine learning model with Multi-Criteria Decision Making (MCDM) and soft computing models integrated with GIS. An Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Fuzzy-AHP, Fuzzy-ANP and AdaBoost machine learning models were developed and integrated with GIS to classify the susceptibility of the study area. Out of 70 sample validation locations, Adaboost’s performance was the best with a 95.72% similarity match with very high and high susceptibility locations followed by F-ANP, ANP, F-AHP and AHP with 95.65%, 92.75%, 81.42% and 77.14% similarity matches, respectively. It also had the highest AUC (0.864). Thus, the Adaboost machine learning, Fuzzy computing and conventional MCDM models can be adopted by stakeholders for accurately assessing flood susceptibility, thereby fostering safe and resilient cities.
dc.language	English
dc.publisher	TAYLOR & FRANCIS LTD
dc.relation.ispartof	Geocarto International
dc.relation.isbasedon	10.1080/10106049.2022.2076910
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.title	Assessment of data mining, multi-criteria decision making and fuzzy-computing techniques for spatial flood susceptibility mapping: a comparative study
dc.type	Journal Article
utslib.citation.volume	37
utslib.for	0909 Geomatic Engineering
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	closed_access	*
dc.date.updated	2023-04-03T01:53:32Z
pubs.issue	26
pubs.publication-status	Published
pubs.volume	37
utslib.citation.issue	26

Abstract:

This study develops an Adaboost-GIS model for flood susceptibility mapping and evaluates its relative performance by undertaking a comparative assessment of the machine learning model with Multi-Criteria Decision Making (MCDM) and soft computing models integrated with GIS. An Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Fuzzy-AHP, Fuzzy-ANP and AdaBoost machine learning models were developed and integrated with GIS to classify the susceptibility of the study area. Out of 70 sample validation locations, Adaboost’s performance was the best with a 95.72% similarity match with very high and high susceptibility locations followed by F-ANP, ANP, F-AHP and AHP with 95.65%, 92.75%, 81.42% and 77.14% similarity matches, respectively. It also had the highest AUC (0.864). Thus, the Adaboost machine learning, Fuzzy computing and conventional MCDM models can be adopted by stakeholders for accurately assessing flood susceptibility, thereby fostering safe and resilient cities.

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http://hdl.handle.net/10453/169053