%0 Journal Article
%@ 10106049
%A Balogun, A.-L.
%A Sheng, T.Y.
%A Sallehuddin, M.H.
%A Aina, Y.A.
%A Dano, U.L.
%A Pradhan, B.
%A Yekeen, S.
%A Tella, A.
%D 2022
%F scholars:17643
%I Taylor and Francis Ltd.
%J Geocarto International
%K 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
%N 26
%P 12989-13015
%R 10.1080/10106049.2022.2076910
%T Assessment of data mining, multi-criteria decision making and fuzzy-computing techniques for spatial flood susceptibility mapping: a comparative study
%U https://khub.utp.edu.my/scholars/17643/
%V 37
%X 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.
%Z cited By 10