Flood Susceptibility Mapping Using GIS-Based Analytic Network Process: A Case Study of Perlis, Malaysia

Abstract

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.

Public perception and attitudes to disaster risks in a coastal metropolis of Saudi Arabia

“…In line with the findings of this study, adopting protective behaviors such as having a working flashlight, a list of emergency phone numbers and teaching relatives and neighbors what to do in emergency cases are the most important approaches for reducing flood risks in the north of Italy [13]. Choices of cautionary and protective behaviors made by persons living in disaster-prone settlements are related to their risk awareness, perception, and evaluation [5].…”

“…Compared to the global North, cities in developing countries are more prone to disaster risks due to rapid urbanization, poorly planned urban expansion, concentrated poverty, poor governance, and environmental degradation [4,5]. Climate change is also expected to intensify the number and severity of disasters far into the future, according to the Intergovernmental Panel on Climate Change [6].…”

Flash Flood Impact Assessment in Jeddah City: An Analytic Hierarchy Process Approach

“…As the MCDM techniques elicit and model experts' flood preferences, they have several advantages, when it comes to evaluating flood impacts and determining risk mitigation measures, over other flood susceptibility assessment techniques such as probabilistic methods, hydrological and stochastic rainfall techniques, and data mining models that are more suitable for assessing flood causative factors [16]. Thus, several studies have utilized the MCDM technique in assessing and mapping flood susceptible zones [17][18][19][20][21]. Among the MCDM techniques, AHP is the most widely used technique in addressing flood disaster challenges because of its ability to solve a wide range of multiple-criteria decision-making problems using a pairwise comparison matrix to generate priority weights for each decision element [22][23][24][25][26][27].…”

Flood Risk Assessment of Global Watersheds Based on Multiple Machine Learning Models

“…However, we were unable to obtain sample points that had not experienced flood disasters from the existing database. The general method was based on existing data; non-flooding sample points were randomly selected in the remaining unrecorded flood areas, but this method often leads to false identification [24][25][26][27]. After all, the existing database cannot accurately record all flood disaster samples.…”

“…Physically based models such as VIC and MIKE models at a regional scale, and other hydrological models at the continental and global scale have also been used to study floods, and have shown great advantages in regional or global flood process research [8,[20][21][22][23]. Recently, machine learning methods such as artificial neural networks (ANN), support vector machines (SVM), and decision trees (DT) have been applied to flood hazard assessments, which can identify and evaluate flood-prone areas based on the training and testing of large amounts of data [24][25][26][27]. By learning the relationship between flooding occurrence and the explanatory factors from the historical flooding records, the machine learning models avoid the subjective determination of weights [28].…”