TY  - JOUR
N1  - cited By 10
SP  - 12989
TI  - Assessment of data mining, multi-criteria decision making and fuzzy-computing techniques for spatial flood susceptibility mapping: a comparative study
AV  - none
EP  - 13015
PB  - Taylor and Francis Ltd.
SN  - 10106049
IS  - 26
N2  - 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, Adaboostâ??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.
ID  - scholars17643
KW  - 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
A1  - Balogun, A.-L.
A1  - Sheng, T.Y.
A1  - Sallehuddin, M.H.
A1  - Aina, Y.A.
A1  - Dano, U.L.
A1  - Pradhan, B.
A1  - Yekeen, S.
A1  - Tella, A.
JF  - Geocarto International
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130606737&doi=10.1080%2f10106049.2022.2076910&partnerID=40&md5=96c3345d3cba70741cb4bb892185f466
VL  - 37
Y1  - 2022///
ER  -