人民珠江2025，Vol.46Issue(4)：39-46,8.DOI:10.3969/j.issn.1001-9235.2025.04.005
基于STL-CEEMDAN-LSTM模型的月径流量预测
Monthly Runoff Prediction Based on STL-CEEMDAN-LSTM Model
摘要
Abstract
According to the nonlinear and non-stationary characteristics of monthly runoff sequences,the quadratic decomposition method was combined with machine learning to construct a model for predicting monthly runoff.This model uses a seasonal trend decomposition procedure based on loess(STL)to decompose the measured monthly runoff sequence into trend terms,seasonal terms,and residual terms with different frequencies.The complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)algorithm was then applied to decompose the residual terms to obtain intrinsic mode functions(IMFs)of different frequency components.Finally,the trend term,seasonal term,and each modal component IMF were used as inputs for the long short term memory network(LSTM)for training and prediction.The model was validated with measured monthly runoff data from Tangnaihai hydrological station in the upper reaches of the Yellow River and was compared and analyzed with other models.The results show that the STL-CEEMDAN-LSTM prediction model has a good simulation effect.The Nash Sutcliffe efficiency(NSE),root mean square error(RMSE),and R2 in the model prediction period are 0.813,239.02,and 0.810,respectively,with the prediction accuracy better than the single model and the primary decomposition model.The secondary decomposition of STL-CEEMDAN can effectively improve the prediction accuracy of the model.关键词
径流预测/模态分解/长短时记忆神经网络/黄河上游Key words
runoff prediction/upper reaches of the Yellow River/modal decomposition/neural networks for short and long-term memory分类
水利科学引用本文复制引用
汪海,沈延青,祁善胜,潘红忠,霍建贞,王战策..基于STL-CEEMDAN-LSTM模型的月径流量预测[J].人民珠江,2025,46(4):39-46,8.基金项目
智慧长江与水电科学湖北省重点实验室开放研究基金项目(242202000923) （242202000923）
