eprintid: 17643
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/76/43
datestamp: 2023-12-19 03:23:59
lastmod: 2023-12-19 03:23:59
status_changed: 2023-12-19 03:08:25
type: article
metadata_visibility: show
creators_name: Balogun, A.-L.
creators_name: Sheng, T.Y.
creators_name: Sallehuddin, M.H.
creators_name: Aina, Y.A.
creators_name: Dano, U.L.
creators_name: Pradhan, B.
creators_name: Yekeen, S.
creators_name: Tella, A.
title: Assessment of data mining, multi-criteria decision making and fuzzy-computing techniques for spatial flood susceptibility mapping: a comparative study
ispublished: pub
keywords: analytical hierarchy process; assessment method; comparative study; data mining; decision making; flood control; fuzzy mathematics; GIS; hazard assessment; machine learning; mapping; remote sensing; spatial analysis
note: cited By 10
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. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
date: 2022
publisher: Taylor and Francis Ltd.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130606737&doi=10.1080%2f10106049.2022.2076910&partnerID=40&md5=96c3345d3cba70741cb4bb892185f466
id_number: 10.1080/10106049.2022.2076910
full_text_status: none
publication: Geocarto International
volume: 37
number: 26
pagerange: 12989-13015
refereed: TRUE
issn: 10106049
citation:   Balogun, A.-L. and Sheng, T.Y. and Sallehuddin, M.H. and Aina, Y.A. and Dano, U.L. and Pradhan, B. and Yekeen, S. and Tella, A.  (2022) Assessment of data mining, multi-criteria decision making and fuzzy-computing techniques for spatial flood susceptibility mapping: a comparative study.  Geocarto International, 37 (26).  pp. 12989-13015.  ISSN 10106049