A Hybrid GIS Multi-Criteria Decision-Making Method for Flood Susceptibility Mapping at Shangyou, China

Abstract

Floods are considered one of the most disastrous hazards all over the world and cause serious casualties and property damage. Therefore, the assessment and regionalization of flood disasters are becoming increasingly important and urgent. To predict the probability of a flood, an essential step is to map flood susceptibility. The main objective of this work is to investigate the use a novel hybrid technique by integrating multi-criteria decision analysis and geographic information system to evaluate flood susceptibility mapping (FSM), which is constructed by ensemble of decision making trial and evaluation laboratory (DEMATEL), analytic network process, weighted linear combinations (WLC) and interval rough numbers (IRN) techniques in the case study at Shangyou County, China. Specifically, we improve the DEMATEL method by applying IRN to determine connections in the network structure based on criteria and to accept imprecisions during collective decision making. The application of IRN can eliminate the necessity of additional information to define uncertain number intervals. Therefore, the quality of the existing data during collective decision making and experts’ perceptions that are expressed through an aggregation matrix can be retained. In this work, eleven conditioning factors associated with flooding were considered and historical flood locations were randomly divided into the training (70% of the total) and validation (30%) sets. The flood susceptibility map validates a satisfactory consistency between the flood-susceptible areas and the spatial distribution of the previous flood events. The accuracy of the map was evaluated by using objective measures of receiver operating characteristic (ROC) curve and area under the curve (AUC). The AUC values of the proposed method coupling with the WLC fuzzy technique for aggregation and flood susceptibility index are 0.988 and 0.964, respectively, which proves that the WLC fuzzy method is more effective for FSM in the study area. The proposed method can be helpful in predicting accurate flood occurrence locations with similar geographic environments and can be effectively used for flood management and prevention.

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

“…There is a dearth of published research that combined GIS-based ANP model and RS to forecast flood susceptible zones in Perlis, Malaysia. Recent studies that adopted the ANP method have not incorporated remote sensing microwave images such as Radar Satellite (RADARSAT) into the analysis [10][11][12]. Moreover, Perlis is in the northern part of Malaysia that is regarded as the "rice bowl" of the country [13].…”

Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques

“…The high importance of remote sensing is due to the fact that these techniques play an essential role in the creation of input datasets for machine learning models. This fact is highlighted also by Wang et al [36] who mentioned that the remote sensing images are a reliable source for flood inventory procedure.…”

Integrating Remote Sensing and Hydrologic Modeling to Assess the Impact of Land-Use Changes on the Increase of Flood Risk: A Case Study of the Riyadh–Dammam Train Track, Saudi Arabia

“…Three multi-criteria decision-making (MCDM) analysis techniques (VIKOR, TOPSIS, and SAW), along with two machine-learning methods (NBT and NB), were tested for their ability to model flood susceptibility. Wang and others [61] investigated the use of a novel hybrid technique by integrating a multi-criteria decision analysis and geographic information system to evaluate flood susceptibility mapping (FSM) in a case study in Shangyou County, China.…”