Flood factor map7/30/2023 Īrora A, Arabameri A, Pandey M, Siddiqui MA, Shukla UK, Bui DT et al (2021) Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain. Sci Total Environ 660:443–458Īrora A, Pandey M, Siddiqui MA, Hong H, Mishra VN (2019) Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models. Comput Geosci 44:120–135Īrabameri A, Rezaei K, Cerdà A, Conoscenti C, Kalantari Z (2019) A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran. Nat Hazards 100:461–491Īlthuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Turkey J Perform Constr Fac 34(1):04019090Īl-Abadi AM, Al-Najar NA (2020) Comparative assessment of bivariate, multivariate and machine learning models for mapping flood proneness. Arab J Geosci 11(11):282Īkay H, Baduna Koçyiğit M (2020) Hydrologic assessment approach for river bridges in Western Black Sea Basin. Baltic J Road Bridge Eng 16(1):37–56Īkay H, Baduna Koçyiğit M, Yanmaz AM (2018) Effect of using multiple stream gauging stations on calibration of hydrologic parameters and estimation of hydrograph of ungauged neighboring basin. ![]() J Flood Risk Manag 14(1):e12683Īkay H (2021) Mitigation of scour failure risk of a river bridge located in an ungauged basin. This study may be considered as a comprehensive contribution to the hybridization methods in predicting accurate flood hazards susceptibility maps.Īhmadlou M, Al-Fugara AK, Al-Shabeeb AR, Arora A, Al-Adamat R, Pham QB, Al-Ansari N, Linh NTT, Sajedi H (2021) Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks. Analysis showed that, IoE model was found to be the best model considering the ROC parameters, while PCA and AHP methods gave more reliable results considering SCAI. Since the predicted results of the methods applied did not point out the same model for each criterion, a simple method was selected to determine the most preferable method. Sensitivity, specificity, accuracy, and kappa index were calculated from ROC analysis, and SCAI was computed from the classification of map by natural break method and flood pixels in that classification. Values at both 70% and 30% of inventory data from the generated maps were extracted to validate the training and testing processes by receiver operating characteristics (ROC) analysis and seed cell area index (SCAI). For this, statistical, and hybrid methods such as frequency ratio (FR), evidential belief function (EBF), weight of evidence (WoE), index of entropy (IoE), fuzzy logic (FL), principal component analysis (PCA), analytical hierarchy process (AHP), technique for order preference by similarity to an ideal solution (TOPSIS), and VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR) were adapted. ![]() In this study, the flood hazards susceptibility map of an area in Turkey which is frequently exposed to flooding was predicted by training 70% of inventory data.
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