中国防汛抗旱2025，Vol.35Issue(2)：74-80,7.DOI:10.16867/j.issn.1673-9264.2024331
基于PSO-TCN深度学习模型的新疆台兰河流域洪水预报研究
Research on flood forecasting in Tailan Watershed of Xinjiang based on PSO-TCN deep learning model
摘要
Abstract
Accurate flood forecasting in advince is beneficial for planning flood measures in watersheds.In this study,a PSO-TCN(Particle Swarm Optimization-Temporal Convolutional Networks)flood forecasting model for the Tailan watershed in Xinjiang Uygur Autonomous Region was constructed by coupling the Particle Swarm Optimization(PSO)algorithm and the Temporal Convolutional Networks(TCN)algorithm.The model was tested using 50 historical flood events,based on observed rainfall-runoff data from 1960 to 2014 in the Tailing watershed.The results showed that under the same lead time conditions,the PSO-TCN model exhibited higher Nash efficiency coefficient,lower root mean square error,and lower relative peak error in flood forecasting.The PSO-TCN flood forecasting model demonstrated better applicability and robustness in the Tailan watershed.However,when the lead time exceeded 5 hours,the relative peak error of the PSO-TCN model still exceeded 20%.In the future,it is expected to integrate the mechanism of flood occurrence process to further improve the generalization ability of deep learning models in flood forecasting.The research results can provide reference for the calculation methods of flood forecasting in watersheds.关键词
洪水预报/深度学习/时间卷积神经网络/粒子群优化算法/PSO-TCN模型/台兰河流域Key words
flood forecasting/deep learning/temporal convolutional networks/particle swarm optimization/PSO-TCN model/Tailan River Basin分类
建筑与水利引用本文复制引用
曹彪,刘敏杰,余其鹰,张廷,马强..基于PSO-TCN深度学习模型的新疆台兰河流域洪水预报研究[J].中国防汛抗旱,2025,35(2):74-80,7.基金项目
新疆自治区重大科技专项(2023A02002-4) （2023A02002-4）
新疆维吾尔自治区自然科学基金项目(2022D01A281). （2022D01A281）
