水利学报2026，Vol.57Issue(8)：1201-1215,15.DOI:10.3724/j.slxb.20250497
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
An integrated deep learning runoff prediction model based on time series decomposition and 2D transformation:methods and its applications
摘要
Abstract
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.关键词
径流预测/集成式模型/时序分解/格拉姆角场/混沌进化优化算法/黄河Key words
runoff prediction/ensemble model/time series decomposition/Gramian Angular Field(GAF)/Chaos-enhanced Evolutionary Optimization(CEO)/the Yellow River basin分类
天文与地球科学引用本文复制引用
徐永康,左德鹏,韩煜娜,马志瑾,刘吉峰,徐宗学..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[J].水利学报,2026,57(8):1201-1215,15.基金项目
国家重点研发计划课题(2021YFC3201104) （2021YFC3201104）
国家自然科学基金项目(52479001) （52479001）
中央高校基本科研业务费专项项目(2253200030) （2253200030）
