水利学报2025，Vol.56Issue(7)：831-843,13.DOI:10.13243/j.cnki.slxb.20240737
耦合气象信息和可解释深度学习方法的日径流预报
Coupling meteorological information and interpretable deep learning methods for daily runoff forecasting
摘要
Abstract
Accurate runoff forecast information is an important basis for flood control and hydropower scheduling.However,factors such as the selection of input features,determination of model structure,and interpretability of forecasting results severely limit the application of data-driven models in runoff forecasting.This study utilizes multi-source data,including observed runoff,rainfall,and ERA5 reanalysis data enriched with comprehensive meteorologi-cal information,as inputs.The framework combines the SHapley Additive exPlanations(SHAP)method with a Con-volutional Neural Network-Bidirectional Long Short-Term Memory(CNN-BiLSTM)model to establish an interpre-table runoff forecasting framework optimized through a post-hoc feature selection mechanism.Firstly,the multi-source data enriches the framework with diverse input information,with CNN and BiLSTM respectively capturing spa-tial correlations and temporal patterns in the data to enhance forecasting accuracy.Secondly,following model train-ing,SHAP calculates the contribution of each input feature.By comparing model performance under different input conditions,optimal input features and model structures are determined.Finally,by quantifying and visualizing the contribution of input features,the framework provides insights into the forecasting mechanism and results.The pro-posed method is applied to interval inflow forecasting at the Tian Sheng Qiao Hydropower Station and compared with models using input features selected by traditional methods such as Partial Autocorrelation Function(PACF),Cross-Correlation Function(CCF),and Random Forest(RF).The results demonstrate that the SHAP-based approach not only optimizes feature selection compared to traditional methods,but also significantly improves the challenges faced by data-driven models in runoff forecasting,including difficulties in input feature selection,inefficiencies,and inter-preting forecasting results.关键词
径流预报/SHAP/CNN-BiLSTM/ERA5/输入特征/可解释性Key words
runoff forecasting/SHAP/CNN-BiLSTM/ERA5/input features/interpretability分类
建筑与水利引用本文复制引用
廖胜利,王在能,程春田,吴慧军..耦合气象信息和可解释深度学习方法的日径流预报[J].水利学报,2025,56(7):831-843,13.基金项目
国家自然科学基金项目(52379004,51979023) （52379004,51979023）
