Assessment of data mining, multi-criteria decision making and fuzzy-computing techniques for spatial flood susceptibility mapping: a comparative study
Abdul‐Lateef Balogun, Tan Yong Sheng, Muhammad Helmy Sallehuddin, Yusuf A. Aina, Umar Lawal Dano, Biswajeet Pradhan, Shamsudeen Temitope Yekeen, Abdulwaheed Tella
Abstract
AbstractThis 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.Keywords: Multi-criteria decision makingGISflood hazardremote sensingflood susceptibilityfuzzy computingmachine learning Disclosure statementNo potential conflict of interest was reported by the authors.Data availability statementAll sources of data have been acknowledged in the manuscript.