基于时序分解与二维转换的集成式深度学习径流预测模型与应用OA

Watershed runoff processes exhibit significant non-stationarity and stochasticity.Point prediction meth-ods,which only provide deterministic results,are inadequate for the urgent demand of uncertainty quantification in risk-based decision-making.To systematically address these two challenges,this study proposed a"Decomposition-Transformation-Identification"integrated deep learning framework for runoff prediction(STL-GAF-CEO-CNN-BiLSTM-ABKDE).This framework first employs Seasonal-Trend decomposition using LOESS(STL)to decompose the runoff series into physically meaningful sub-series.Subsequently,Gramian Angular Field(GAF)is utilized to convert the one-dimensional sub-series into two-dimensional images to extract their deep morphological features.A hybrid architecture of Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory network(BiLSTM)then performs deep recognition and prediction,with its hyperparameters optimized by a Chaos-enhanced Evolutionary Optimization(CEO)algorithm.Finally,the Adaptive Bandwidth Kernel Density Estimation(ABKDE)method is adopted to quantify prediction uncertainty.The proposed framework was validated using monthly runoff data from three typical hydrological stations on the main stream of the Yellow River.The results demonstrate that the integrated framework outperformed all 12 comparison models,achieving Nash-Sutcliffe Efficiency(NSE)coeffi-cients of 0.91,0.89,and 0.87 at the Tangnaihai,Sanmenxia,and Lijin stations,respectively.In terms of uncer-tainty quantification,the framework's comprehensive evaluation metric,the Coverage Width-based Criterion(CWC),was reduced by 20.6％-29.9％at a 95％confidence level compared to the fixed-bandwidth method,achieving a better balance between prediction reliability and accuracy in interval prediction.The integrated framework proposed in this study presents a novel approach for precise runoff prediction and uncertainty quantification under complex con-ditions,providing a valuable reference for water resource risk management.