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Daily Runoff Forecasting in the Middle Yangtze River Using a Long Short-Term Memory Network Optimized by the Sparrow Search Algorithm

文献类型：期刊论文

作者Zhang, Qi2; Dong, Yaoyao2; Zhan, Chesheng1; Wang, Yueling3; Wang, Hongyan2; Zou, Hongxia2
刊名WATER
出版日期2026-01-31
卷号18期号:3页码:364
关键词runoff prediction Long Short-Term Memory (LSTM) Sparrow Search Algorithm (SSA) hyperparameter optimization middle reaches of the Yangtze River Jiujiang Station
DOI10.3390/w18030364
产权排序3
文献子类Article
英文摘要To address the challenge of predicting runoff processes in the middle reaches of the Yangtze River under the influence of complex river-lake relationships and human disturbances, this paper proposes a coupled model based on the Sparrow Search Algorithm-optimized Long Short-Term Memory neural network (SSA-LSTM) for daily runoff forecasting at the Jiujiang Hydrological Station. The input data were preprocessed through feature selection and sequence decomposition. Subsequently, the Sparrow Search Algorithm (SSA) was utilized to perform automated of key hyperparameters of the Long Short-Term Memory (LSTM) model, thereby enhancing the model's adaptability under complex hydrological conditions. Experimental results based on multi-station hydrological and meteorological data of the middle reaches of the Yangtze River from 2009 to 2016 show that the SSA-LSTM achieves a Nash-Sutcliffe Efficiency (NSE) of 0.98 during the testing period (2016). The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are reduced by 49.3% and 51.3%, respectively, compared to the standard LSTM. A comprehensive evaluation across different flow levels, utilizing Taylor diagrams and error distribution analysis, further confirms the model's robustness. The model demonstrates robust performance across different flow regimes: compared to the standard LSTM model, SSA-LSTM improves the NSE from 0.45 to 0.88 in high-flow scenarios, exhibiting excellent capabilities in peak flow prediction and flood process characterization. In low-flow scenarios, the NSE is improved from -0.77 to 0.72, indicating more reliable prediction of baseflow mechanisms. The study demonstrates that SSA-LSTM can effectively capture hydrological nonlinear characteristics under strong river-lake backwater and human disturbances, providing a high-precision and high-efficiency data-driven method for runoff prediction in complex basins.
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WOS研究方向Environmental Sciences & Ecology ; Water Resources
语种英语
WOS记录号WOS:001687583900001
出版者MDPI
源URL[http://ir.igsnrr.ac.cn/handle/311030/220916]  
专题陆地水循环及地表过程院重点实验室_外文论文
通讯作者Dong, Yaoyao; Zhan, Chesheng
作者单位1.Chinese Acad Sci, Inst Atmospher Phys, Beijing 100029, Peoples R China;
2.Space Engn Univ, Sch Space Informat, Beijing 101416, Peoples R China;
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Qi,Dong, Yaoyao,Zhan, Chesheng,et al. Daily Runoff Forecasting in the Middle Yangtze River Using a Long Short-Term Memory Network Optimized by the Sparrow Search Algorithm[J]. WATER,2026,18(3):364.
APA Zhang, Qi,Dong, Yaoyao,Zhan, Chesheng,Wang, Yueling,Wang, Hongyan,&Zou, Hongxia.(2026).Daily Runoff Forecasting in the Middle Yangtze River Using a Long Short-Term Memory Network Optimized by the Sparrow Search Algorithm.WATER,18(3),364.
MLA Zhang, Qi,et al."Daily Runoff Forecasting in the Middle Yangtze River Using a Long Short-Term Memory Network Optimized by the Sparrow Search Algorithm".WATER 18.3(2026):364.

入库方式： OAI收割

来源：地理科学与资源研究所

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