A Novel Hybrid Extreme Learning Machine Approach Improved by K Nearest Neighbor Method and Fireworks Algorithm for Flood Forecasting in Medium and Small Watershed of Loess Region

被引:25
作者
Ren, Juanhui [1 ]
Ren, Bo [2 ,3 ]
Zhang, Qiuwen [2 ]
Zheng, Xiuqing [1 ]
机构
[1] Taiyuan Univ Technol, Coll Water Resources Sci & Engn, Taiyuan 030024, Shanxi, Peoples R China
[2] Huazhong Univ Sci & Technol, Coll Hydropower & Informat Engn, Wuhan 430074, Hubei, Peoples R China
[3] Hydrol Bur Shanxi Prov, Taiyuan 030001, Shanxi, Peoples R China
关键词
loess region; medium and small watershed; flood forecasting; k nearest neighbor method; fireworks algorithm; extreme learning machine; hybrid approach; RAINFALL PROBABILISTIC FORECASTS; ARTIFICIAL NEURAL-NETWORK; SUPPLY MANAGEMENT; COMPUTATIONAL INTELLIGENCE; RIVER FLOW; MODEL; OPTIMIZATION; PREDICTION; REGRESSION; SCIENCES;
D O I
10.3390/w11091848
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Sudden floods in the medium and small watershed by a sudden rainstorm and locally heavy rainfall often lead to flash floods. Therefore, it is of practical and theoretical significance to explore appropriate flood forecasting model for medium and small watersheds for flood control and disaster reduction in the loess region under the condition of underlying surface changes. This paper took the Gedong basin in the loess region of western Shanxi as the research area, analyzing the underlying surface and floods characteristics. The underlying surface change was divided into three periods (HSP1, HSP2, HSP3), and the floods were divided into three grades (great, moderate, small). The paper applied K Nearest Neighbor method and Fireworks Algorithm to improve the Extreme Learning Machine model (KNN-FWA-ELM) and proposed KNN-FWA-ELM hybrid flood forecasting model, which was further applied to flood forecasting of different underlying surface conditions and flood grades. Results demonstrated that KNN-FWA-ELM model had better simulation performance and higher simulation accuracy than the ELM model for flood forecasting, and the qualified rate was 17.39% higher than the ELM model. KNN-FWA-ELM model was superior to the ELM model in three periods and the simulation performance of three flood grades, and the simulation performance of KNN-FWA-ELM model was better in HSP1 stage floods and great floods.
引用
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页数:31
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