湖泊科学2024，Vol.36Issue(1)：320-332,13.DOI:10.18307/2024.0143
基于鲸鱼优化算法的长短期记忆模型水库洪水预报
Flood forecasting method for reservoirs based on WOA-LSTM
摘要
Abstract
Flood is one of the world's major natural disasters.Optimizing flood forecasting solutions is crucial for flood management decisions.However,traditional hydrological models suffer from issues such as a high number of parameters,susceptibility to human interference during parameter calibration,and weak generalization ability.To address these challenges,this study proposes a WOA-LSTM model that integrates an improved whale optimization algorithm and long short-term memory network to automatically optimize model parameters.Furthermore,the stability and accuracy of the model are further enhanced through the optimization of the neural network structure.Additionally,flood forecasting models with different lead times are established to analyze and discuss the rela-tionship between the neural network structure and the forecast period.By selecting the Hengjin Reservoir basin as a case study,the rainfall data of seven stations and the hydrological data of Hengjin Station during 1986-1997 were collected as model inputs.The flood process under different forecast periods was model output with the calibration and validation periods of 1986-1993 and 1994-1997,respectively.The modeling results showed that:(1)Compared with the LSTM model and the XAJ model,the WOA-LSTM model achieved a better performance evaluated by the peak-present time difference,deterministic coefficient,relative error in runoff depth and peak flow error.(2)By displacing the eigenvalues,the SHAP method analyzed the importance of the model eigenval-ues,and thus enhanced the interpretability of the neural network model.(3)By appropriately designing the neural network struc-ture,the problem of model prediction accuracy deteriorating due to the decrease of data correlation caused by the increase of the forecast period can be avoided to a certain extent.The case study demonstrated that the model can meet the requirements of flood forecasting for Hengjin Reservoir during the forecast period of 1-6 h,and can support managers to achieve best management prac-tices in controlling flood.关键词
洪水预报/长短期记忆模型(LSTM)/鲸鱼优化算法/深度学习Key words
Flood forecasting/long-short term memory(LSTM)/whale optimization algorithms/deep learning引用本文复制引用
丁艺鼎,蒋名亮,徐力刚,范宏翔,吕海深..基于鲸鱼优化算法的长短期记忆模型水库洪水预报[J].湖泊科学,2024,36(1):320-332,13.基金项目
国家自然科学基金项目(41971137,U2240224,42001109)、江苏省自然科学基金项目(BK20201102)和江西省科技计划项目(20213AAG01012,20212BBG71002,20222BCD46002)联合资助. （41971137,U2240224,42001109）
