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

Himan Shahabi; Ataollah Shirzadi; Somayeh Ronoud; Shahrokh Asadi; Binh Thai Pham; Fatemeh Mansouripour; Marten Geertsema; John J.Clague; Dieu Tien Bui

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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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Flash floods are responsible for loss of life and considerable property damage in many countries.Flood susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by landuse planners and emergency managers.The 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)technique.Various well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed model.Statistical 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 model.The 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.

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《地学前缘：英文版》 |2021年第3期|146-168|共23页
作者
Himan Shahabi; Ataollah Shirzadi; Somayeh Ronoud; Shahrokh Asadi; Binh Thai Pham; Fatemeh Mansouripour; Marten Geertsema; John J.Clague; Dieu Tien Bui;
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作者单位

Department of Geomorphology;

Faculty of Natural Resources;

University of Kurdistan;

Sanandaj 66177-15175;

Iran;

Board Member of Department of Zrebar Lake Environmental Research;

Kurdistan Studies Institute;

University of Kurdistan;

Sanandaj 66177-15175;

Iran;

Department of Rangeland and Watershed Management;

Faculty of Natural Resources;

University of Kurdistan;

Sanandaj;

Iran;

Data Mining Laboratory;

Department of Engineering;

College of Farabi;

University of Tehran;

Tehran;

Iran;

Institute of Research and Development;

Duy Tan University;

Da Nang 550000;

Viet Nam;

British Columbia;

Ministry of Forests;

Lands;

Natural Resource Operations and Rural Development;

Prince George;

BC V2L 1R5;

Canada;

Department of Earth Sciences;

Simon Fraser University;

Burnaby;

BC V5A 1S6;

Canada;

Geographic Information Science Research Group;

Ton Duc Thang University;

Ho Chi Minh City;

Viet Nam;

Faculty of Environment and Labour Safety;

Ton Duc Thang University;

Ho Chi Minh City;

Viet Nam;

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原文格式 PDF
正文语种 chi
中图分类计算技术、计算机技术;
关键词
Environmental modeling; Flash flood; Deep belief network; Over-fitting; Iran;

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

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