江西水利电力大学学报2026，Vol.45Issue(4)：10-20,11.
基于 Hyperband-PatchTST模型的河流流量及水位预测
Prediction of streamflow and water level based on Hyperband-PatchTST model
摘要
Abstract
The prediction of streamflow and water level is crucial for water resource management,flood control and disaster mitigation,and watershed ecological regulation.To improve the accuracy and efficiency of such predictions,this study uses measured data from monitoring stations on the San Juan River and the Colorado River as the research object.A deep learn-ing model integrating Hyperband and PatchTST is constructed and compared with models such as LSTM,GRU,and ANN.The Hyperband optimization algorithm tunes the hyperparameters of the deep learning model,reducing model training costs and computational resource consumption.PatchTST divides the hydrological data input into local segments(Patches)and u-ses the Self-Attention mechanism to capture local temporal features and nonlinear relationships in the data,thereby impro-ving the model's computational efficiency,accuracy,and stability,and enabling precise prediction of stream flow and water level.The results show that Hyperband-PatchTST model achieves optimal performance across all evaluation metrics.Com-pared with the suboptimal model,its MAE and RMSE are reduced by approximately 6%～12% and 2%～8%,respective-ly,and its R2 decays more slowly over time.It indicates that the model offers higher prediction accuracy,stronger stability,and better generalization ability,providing an efficient and reliable new method for prediction of stream flow and water level.关键词
河流流量/水位预测/Hyperband-PatchTST模型/超参数优化/深度学习Key words
streamflow prediction/water level prediction/Hyperband-PatchTST model/hyperparameter optimization/deep learning分类
建筑与水利引用本文复制引用
陈舒泰,彭友文..基于 Hyperband-PatchTST模型的河流流量及水位预测[J].江西水利电力大学学报,2026,45(4):10-20,11.基金项目
国家自然科学基金项目(52579124) （52579124）
