湖泊科学2024，Vol.36Issue(4)：1241-1251,中插30,12.DOI:10.18307/2024.0454
考虑记忆时间的LSTM模型在赣江流域径流预报中的应用
Application of LSTM considering time steps in runoff prediction of Ganjiang River Basin
摘要
Abstract
Accurate forecast of runoff is important for water resources management and planning under climate change.This study developed a Long Short-Term Memory(LSTM)neural network model for runoff forecasting at three catchments(Waizhou,Xia-jiang,and Dongbei)in Ganjiang River Basin.Based on the developed LSTM,we analyzed the relationship between the effective lead time of the model and watershed average transit time of different basins,between the accuracy of the LSTM runoff prediction model and time steps during the effective lead time,between different lead times and the best time steps of the model,and the rela-tionship between the time steps required for LSTM runoff prediction and the watershed area.The results showed that(1)the best model fit can be achieved by considering both precipitation and antecedent runoff.The Nash-Sutcliffe efficiency coefficient(NSE)for the Waizhou,Xiajiang and Dongbei stations reached 0.98,0.96 and 0.90,respectively under 1-day lead time.Moreover,the effective lead time is the same as the one which only considering the information of precipitation,and both are close to the water-shed average transit time.(2)By extending the lead time,the model fit decreased under different scenarios,with the largest de-creasing rate from the scenario that only considered the antecedent runoff.This indicated that precipitation was more important for runoff prediction.Additionally,the accuracy of runoff prediction increased with larger watershed area.(3)By fixing the lead time,the model fit firstly improved,then decreased to gradually stabilize with the extension of the time steps.During the effective lead time,the best time steps increased when extending the lead time,and the best time steps were 14 days for the longest effective lead time.The best time steps of LSTM decreased with a larger catchment area in the Ganjiang River Basin.The results can be taken as a reference for runoff forecasting in Ganjiang River Basin,and are helpful for obtaining the best time steps of machine learning or runoff prediction in other basins.关键词
LSTM模型/赣江流域/记忆时间/径流预测/预见期Key words
LSTM model/Ganjiang River Basin/time steps/runoff prediction/lead time引用本文复制引用
胡乐怡,蒋晓蕾,周嘉慧,欧阳芬,戴逸姝,章丽萍,付晓雷..考虑记忆时间的LSTM模型在赣江流域径流预报中的应用[J].湖泊科学,2024,36(4):1241-1251,中插30,12.基金项目
国家自然科学基金项目(42371021,52109036)、河海大学水灾害防御全国重点实验室"一带一路"水与可持续发展科技基金面上项目(2022491111,2021490611)、水利部水文气象灾害机理与预警重点实验室开放基金(HYMED202203,HYMED202210)和江苏省研究生科研与实践创新计划项目(KYCX23_3546,KYCX23_3549)联合资助. （42371021,52109036）
