水科学进展2025，Vol.36Issue(4)：581-595,15.DOI:10.14042/j.cnki.32.1309.2025.04.004
基于可解释多源数据特征融合的深度学习集合径流预测
Deep learning ensemble streamflow prediction based on explainable multi-source data feature fusion
摘要
Abstract
Accurate streamflow prediction holds pivotal implications for water resources management and flood early warning.Nonetheless,the highly nonlinear nature of streamflow processes poses significant challenges to conventional models,which also demonstrate insufficient integration of spatiotemporal features and deficiency of interpretability.In this study,24 types of multi-source heterogeneous data underwent systematic investigation,comprising remote sensing and meteorological data.Aside from that,the impacts of human activities and climate change were comprehensively considered to construct a high-precision and interpretable Transformer-KAN-LEC(TKL)deep learning ensemble streamflow prediction model.Taking daily streamflow prediction at 11 stations in the Jialingjiang River basin as a case study,the experimental findings illustrate that:the Nash-Sutcliffe efficiency coefficient(ENS)of the TKL model is all greater than 0.95,the root mean square error(ERMS)is reduced by 40%—80%in contrast to traditional models,and both the interval prediction and extreme event prediction performances are superior to traditional models.Interpretability analysis reveals that upstream streamflow and cumulative precipitation effects are the dominant influencing factors.The"data-model-interpretation"systematic framework recommended in this paper can offer adequate and continuous support for water resources management and precise flood early warning in large basins.关键词
径流预测/深度学习集合模型/时空特征融合/区间预测/注意力机制Key words
streamflow prediction/deep learning ensemble model/spatiotemporal feature fusion/interval prediction/attention mechanism分类
建筑与水利引用本文复制引用
丁诚,王兆才,丁伟杰,程和琴..基于可解释多源数据特征融合的深度学习集合径流预测[J].水科学进展,2025,36(4):581-595,15.基金项目
国家自然科学基金项目(11701363) （11701363）
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(IWHRSEDI-2023-10)The study is financially supported by the National Natural Science Foundation of China(No.11701363)and Open Fund Project of Key Laboratory of Sedimentology Science and Northern River Management,Ministry of Water Resources,China(No.IWHRSEDI-2023-10). （IWHRSEDI-2023-10）
