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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.
Wang et al. (Sun,) studied this question.