Flood Susceptibility Mapping Using GIS-Based Analytic Network Process: A Case Study of Perlis, Malaysia
Umar Lawal Dano,
Abdul-Lateef Balogun,
Abdul-Nasir Matori,
Khmaruzzaman Wan Yusouf,
Ismaila Rimi Abubakar,
Mohamed Ahmed Said Mohamed,
Yusuf Adedoyin Aina,
Biswajeet Pradhan
Affiliations
Umar Lawal Dano
Department of Urban & Regional Panning, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 32141, Saudi Arabia
Abdul-Lateef Balogun
Geospatial Analysis and Modelling Research Group, Department of Civil & Environmental Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak Darul Ridzuan, Malaysia
Abdul-Nasir Matori
Geospatial Analysis and Modelling Research Group, Department of Civil & Environmental Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak Darul Ridzuan, Malaysia
Khmaruzzaman Wan Yusouf
Geospatial Analysis and Modelling Research Group, Department of Civil & Environmental Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak Darul Ridzuan, Malaysia
Ismaila Rimi Abubakar
College of Architecture and Planning, Imam Abdulrahman Bin Faisal University (formerly, University of Dammam), P.O. Box 1982, Dammam 32141, Saudi Arabia
Mohamed Ahmed Said Mohamed
Architectural Department, College of Architecture, Sudan University of Science and Technology, Khartoum P.O. Box 11111, Sudan
Yusuf Adedoyin Aina
Department of Geomatics Engineering Technology, Yanbu Industrial College, Yanbu 41912, Saudi Arabia
Biswajeet Pradhan
Center for Advanced Modeling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia
Understanding factors associated with flood incidence could facilitate flood disaster control and management. This paper assesses flood susceptibility of Perlis, Malaysia for reducing and managing their impacts on people and the environment. The study used an integrated approach that combines geographic information system (GIS), analytic network process (ANP), and remote sensing (RS) derived variables for flood susceptibility assessment and mapping. Based on experts’ opinion solicited via ANP survey questionnaire, the ANP mathematical model was used to calculate the relative weights of the various flood influencing factors. The ArcGIS spatial analyst tools were used in generating flood susceptible zones. The study found zones that are very highly susceptible to flood (VHSF) and those highly susceptible to flood (HSF) covering 38.4% (30,924.6 ha) and 19.0% (15,341.1 ha) of the study area, respectively. The results were subjected to one-at-a-time (OAT) sensitivity analysis to verify their stability, where 6 out of the 22 flood scenarios correlated with the simulated spatial assessment of flood susceptibility. The findings were further validated using real-life flood incidences in the study area obtained from satellite images, which confirmed that most of the flooded areas were distributed over the VHSF and HSF zones. This integrated approach enables network model structuring, and reflects the interdependences among real-life flood influencing factors. This accurate identification of flood prone areas could serve as an early warning mechanism. The approach can be replicated in cities facing flood incidences in identifying areas susceptible to flooding for more effective flood disaster control.
