A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique

• Published in: Natural hazards (Dordrecht) 2016-09, Vol.83 (2), p.947-987
• Main Authors: Khosravi, Khabat, Nohani, Ebrahim, Maroufinia, Edris, Pourghasemi, Hamid Reza
• Format: Article
• Language: English
• Subjects: Analytic hierarchy process; Assessments; Civil Engineering; Disasters; Earth and Environmental Science; Earth Sciences; Environmental Management; Flood mapping; Floods; Freshwater; Geographic information systems; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Mathematical models; Natural disasters; Natural Hazards; Original Paper; Statistical methods; Statistical models; Vegetation; Watersheds
• ISSN: 0921-030X; 1573-0840
• DOI: 10.1007/s11069-016-2357-2

Flood is one of the most prevalent natural disasters that frequently occur in the northern part of Iran reported in hot spots of flood occurrences. The main aim of the current study was to prepare flood susceptibility maps using four models, namely frequency ratio (FR), weights-of-evidence (WofE), analytical hierarchy process (AHP), and ensemble of frequency ratio with AHP (FR-AHP), and to compare them at Haraz Watershed in Mazandaran Province, Iran. A total of 211 flood locations were prepared in GIS environment, of which 151 locations were randomly selected for modeling and the remaining 60 locations were used for validation aims. In the next step, 10 flood-conditioning factors were prepared including slope angle, plan curvature, elevation, topographic wetness index, stream power index, rainfall, distance from river, geology, landuse, and normalized difference vegetation index. The receiver operating characteristic curve and the area under the curve (AUC) were created for different flood susceptibility maps. Validation of results showed that AUC values for success rate in training data set, for FR, WofE, AHP, and FR-AHP, were 97.07, 98.96, 95.91, and 86.19 % with prediction rates of 0.9657 (96.57 %), 0.9596 (95.96 %), 0.9492 (94.92 %), and 0.8469 (84.69 %), respectively. Moreover, the results showed that the frequency ratio model had the highest AUC in comparison with other models. Generally, the four models show a reasonable accuracy in flood-susceptible areas. The results of this study can be useful for managers, researchers, and planners to manage the susceptible areas to flood and reduce damages.