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Flash flood susceptibility mapping using a novel deep learning model based on deep belief network,back propagation and genetic algorithm

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Geoscience Frontiers 2021年第3期12卷 146-168页

作者： Himan Shahabi Ataollah Shirzadi Somayeh Ronoud shahrokh asadi Binh Thai Pham Fatemeh Mansouripour Marten Geertsema John J.Clague Dieu Tien Bui Department of Geomorphology Faculty of Natural ResourcesUniversity of KurdistanSanandaj 66177-15175Iran Board Member of Department of Zrebar Lake Environmental Research Kurdistan Studies InstituteUniversity of KurdistanSanandaj 66177-15175Iran Department of Rangeland and Watershed Management Faculty of Natural ResourcesUniversity of KurdistanSanandajIran Data Mining Laboratory Department of EngineeringCollege of FarabiUniversity of TehranTehranIran Institute of Research and Development Duy Tan UniversityDa Nang 550000Viet Nam British Columbia Ministry of ForestsLandsNatural Resource Operations and Rural DevelopmentPrince GeorgeBC V2L 1R5Canada Department of Earth Sciences Simon Fraser UniversityBurnabyBC V5A 1S6Canada Geographic Information Science Research Group Ton Duc Thang UniversityHo Chi Minh CityViet Nam Faculty of Environment and Labour Safety Ton Duc Thang UniversityHo Chi Minh CityViet Nam

Flash floods are responsible for loss of life and considerable property damage in many *** susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by landuse planners and emergency *** main objective of this study is to prepare an accurate flood susceptibility map for the Haraz watershed in Iran using a novel modeling approach(DBPGA)based on Deep Belief Network(DBN)with Back Propagation(BP)algorithm optimized by the Genetic Algorithm(GA).For this task,a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Evaluation(ORAE)*** well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed *** metrics include sensitivity,specificity accuracy,root mean square error(RMSE),and area under the receiver operatic characteristic curve(AUC)were used to assess the validity of the proposed *** result shows that the proposed model has the highest goodness-of-fit(AUC=0.989)and prediction accuracy(AUC=0.985),and based on the validation dataset it outperforms benchmark models including LR(0.885),LMT(0.934),BLR(0.936),ADT(0.976),NBT(0.974),REPTree(0.811),ANFIS-BAT(0.944),ANFIS-CA(0.921),ANFIS-IWO(0.939),ANFIS-ICA(0.947),and ANFIS-FA(0.917).We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods.

关键词： Environmental modeling Flash flood Deep belief network Over-fitting Iran

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