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Remote Sens. 2019, 11(1), 62;

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

Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing 210023, Jiangsu, China
College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, Shanxi, China
State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, Hubei, China
Department of Logistics, University of Defence, 11000 Belgrade, Serbia
Department of Geography, University of Defence, 11000 Belgrade, Serbia
Military Geographical Institute, 11000 Belgrade, Serbia
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, N-3800 Bø i Telemark, Norway
Author to whom correspondence should be addressed.
Received: 28 November 2018 / Revised: 24 December 2018 / Accepted: 28 December 2018 / Published: 30 December 2018
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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. View Full-Text
Keywords: flood susceptibility; GIS modeling; multi-criteria decision-making; interval rough numbers; expert knowledge flood susceptibility; GIS modeling; multi-criteria decision-making; interval rough numbers; expert knowledge

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Wang, Y.; Hong, H.; Chen, W.; Li, S.; Pamučar, D.; Gigović, L.; Drobnjak, S.; Tien Bui, D.; Duan, H. A Hybrid GIS Multi-Criteria Decision-Making Method for Flood Susceptibility Mapping at Shangyou, China. Remote Sens. 2019, 11, 62.

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