水利学报2025，Vol.56Issue(6)：717-725,9.DOI:10.13243/j.cnki.slxb.20240630
基于深度学习的长江-洞庭湖流域洪水模拟预报研究
Flood simulation and forecasting in the Yangtze River-Dongting Lake Basin based on deep learning
摘要
Abstract
Accurate and fast flood forecasting in the Yangtze River-Dongting Lake basin is vitally important for flood control operation of the Three Gorges Reservoir.Three deep learning models,namely the Convolutional Neural Net-work(CNN),the Long Short-Term Memory neural network(LSTM),and the Gated Recurrent Unit(GRU),were constructed.The recorded flow discharges and rainfalls of eight hydrological stations and 301 rain gauge stations during flood season(from May to September)from 2010 to 2023 were used to calibrate and verify these models.The results show that the flood simulation and forecasting accuracy of these deep learning models are all sufficiently high.Among them,the GRU model performs the best,with the Nash-Sutcliffe Efficiency(NSE)in the 24-hour forecast horizon reaching 0.993 during the training period and 0.988 during the testing period,and the total runoff relative errors being 0.25％and-0.26％,respectively.For the 6 flood events with peak flows exceeding 20,000 m3/s during the testing period,the forecast results of the three models vary significantly.The GRU model is superior to LSTM and CNN models,with the NSE values greater than 0.85,and the absolute errors of peak flow less than 2％.The GRU and LSTM models have high simulation accuracy and strong generalization ability,which can provide a new approach for complex flood forecasting in the Yangtze River-Dongting Lake Basin.关键词
洪水预报/深度学习/卷积神经网络/长短期记忆神经网络/门控单元神经网络/长江-洞庭湖流域Key words
hydrological forecasting/deep learning/convolutional neural network/Long Short-Term Memory neural network/Gated Recurrent Unit/Yangtze River-Dongting Lake Basin分类
天文与地球科学引用本文复制引用
李承龙,郭生练,梁志明,崔震,向鑫,张俊..基于深度学习的长江-洞庭湖流域洪水模拟预报研究[J].水利学报,2025,56(6):717-725,9.基金项目
国家自然科学基金长江联合基金项目(U2340205) （U2340205）
中国长江电力股份公司科研项目(Z242402005) （Z242402005）
