中国水利Issue(14)：56-65,10.DOI:10.3969/j.issn.1000-1123.2025.14.010
基于机器学习方法的东江三角洲咸潮长期预测模型研究
Research on a long-term salinity intrusion forecasting model for the Dongjiang River Delta based on machine learning approaches
摘要
Abstract
Long-term hydrological forecasting is a key area in hydrology and water resources management.It provides essential support for water resources planning,flood warning,agricultural irrigation,and urban water supply by predicting hydrological variables over extended time periods.Focusing on the key challenges in water resources allocation under salinity intrusion,this study investigates the application of machine learning-based salinity intrusion forecasting models in the Dongjiang River Delta.Emphasis is placed on the selection of characteristic factors and the medium-to long-term prediction of salinity intrusion on an interannual scale under varying conditions.A salinity intrusion impact evaluation system is developed to identify critical characteristic factors.Random forest,gradient boosting tree,and ensemble models are employed for forecasting.The results indicate that non-flood season rainfall,end-of-flood season storage in Longtan Reservoir,diversion ratio at Sanshui Station,and end-of-flood season storage in Xinfengjiang Reservoir are the key factors influencing salinity intrusion.The ensemble model constructed with these critical factors enhances the accuracy and stability of salinity intrusion forecasting.Additionally,predictions conducted under multiple practical scenarios provide scientific support for water resources management and salinity intrusion warning in the Dongjiang River Delta.关键词
咸潮评价体系/机器学习/特征因子/咸潮预测模型/东江三角洲Key words
salinity intrusion evaluation system/machine learning/characteristic factors/salinity intrusion forecasting model/Dongjiang River Delta分类
建筑与水利引用本文复制引用
谢雨航,廖梓瑾,王晨乃,周喆,王京晶..基于机器学习方法的东江三角洲咸潮长期预测模型研究[J].中国水利,2025,(14):56-65,10.基金项目
广东省水利科技创新项目"北江流域-城市复合洪涝过程模拟与风险评估"(2025-23). （2025-23）
