水利学报2025，Vol.56Issue(2)：240-252,265,14.DOI:10.13243/j.cnki.slxb.20240168
基于深度学习集合优化模型的径流区间预测研究
Research on streamflow interval prediction based on deep learning ensemble optimization model
摘要
Abstract
Due to the increasing occurrence of extreme weather events and the complexity of streamflow varia-tions,it is challenging to realize accurate streamflow prediction,and previous studies are mostly based on point prediction of determinate values,which is hard to take into account the effect of uncertainty and lead to the lack of practical applicability of the prediction results.In this study,a deep learning ensemble model for streamflow interval prediction based on meteorological and hydrological variables is developed.The model first filters out the key driving variables affecting streamflow through the Pearson correlation coefficient(PCC).Then the raw data are decomposed into intrinsic mode functions(IMFs)by variational modal decomposition(VMD).The compo-nents are then quadratically decomposed using complementary ensemble empirical modal decomposition(CEEMD)to capture more details of the data.The point prediction results of streamflow are obtained by a gated recurrent unit(GRU)incorporating an attention mechanism(AM),and an improved sparrow search algorithm(ISSA)is used to optimize hyperparameters such as the learning rate of the GRU and the number of hidden layer dimensions to enhance the model performance.Finally,nonparametric kernel density estimation(NKDE)is in-troduced for interval prediction.The combined model VMD-CEEMD-ISSA-AM-GRU(VCIAG)constructed in this study performs advance multi-period prediction for nine hydrological stations in the Jialing River basin.The results of the study showed that the model performs well in the short term,with Nash efficiency coefficients(NSE)close to 1 for several stations.For flood forecasting,the model's NSE for Dongjintuo,Wusheng,and Jinxi stations are 0.73,0.92,and 0.92,respectively.In addition,the effects of the input variables on runoff are quantified by the Shapley value method(Shapley).The VCIAG model proposed in this study not only per-forms well in streamflow prediction accuracy,but also has significant advantages in interval prediction of uncer-tainty,which can provide managers with more accurate and reliable runoff information,and thus better support runoff risk assessment and scientific decision-making programs in practice.关键词
深度学习集合模型/径流区间预测/模态分解/改进的麻雀优化算法/注意力机制Key words
deep learning ensemble models/streamflow interval prediction/modal decomposition/improved sparrow search algorithm/attention mechanism分类
天文与地球科学引用本文复制引用
黄靖涵,王兆才,吴俊豪,姚之远..基于深度学习集合优化模型的径流区间预测研究[J].水利学报,2025,56(2):240-252,265,14.基金项目
国家自然科学基金项目(11701363,52279079) （11701363,52279079）
中国水利水电科学研究院流域水循环模拟与调控国家重点实验室开放研究基金项目(IWHR-SKL-201905) （IWHR-SKL-201905）
水利部泥沙科学与北方河流治理重点实验室开放基金项目(IWHR-SEDI-2023-10) （IWHR-SEDI-2023-10）
