人民长江2025，Vol.56Issue(4)：128-135,8.DOI:10.16232/j.cnki.1001-4179.2025.04.017
基于CEEMDAN-IASO-TCN组合模型的中长期径流预报
Medium and long-term runoff forecasting based on CEEMDAN-IASO-TCN combined model
摘要
Abstract
Accurate prediction of monthly runoff is crucial for water resource management in a watershed.In order to enhance the accuracy of medium and long-term runoff prediction,a CEEMDAN-IASO-TCN combined model is proposed,which is con-structed by combining complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),improved atomic search algorithm(IASO),and temporal convolutional network(TCN).The model firstly uses CEEMDAN to decompose the monthly runoff sequence,and then uses IASO to optimise the batch size,learning rate,and discard factor of the TCN model to ob-tain the optimal time convolution network structure and predict the components using the optimal IASO-TCN,and finally recon-structs the component prediction results to obtain the final monthly runoff prediction results.The monthly runoff data from 1957 to 2019 at Zhenjiangguan Hydrological Station in Minjiang River Basin are taken as the study object,and the proposed model is com-pared with other models.The results show that the CEEMDAN-IASO-TCN model has the highest prediction accuracy,with Nash coefficients of 0.919 1 and 0.869 1 in the training and testing stages,respectively.The research results can provide a relia-ble basis for the sustainable use of water resources.关键词
中长期径流预报/自适应噪声完备集合经验模态分解/原子搜索算法/时间卷积网络/岷江流域Key words
medium and long-term runoff forecasting/complete ensemble empirical mode decomposition with adaptive noise/atomic search algorithm/temporal convolutional network/Minjiang River Basin分类
天文与地球科学引用本文复制引用
徐军杨,罗远林,刘月馨,陈冬强,张坚,张楚..基于CEEMDAN-IASO-TCN组合模型的中长期径流预报[J].人民长江,2025,56(4):128-135,8.基金项目
国家自然科学基金项目(62303191,62306123) （62303191,62306123）
江苏省自然科学基金项目(BK20191052) （BK20191052）
江苏省高校自然科学基金面上项目(23KJD480001) （23KJD480001）
