长沙理工大学学报（自然科学版）2025，Vol.22Issue(1)：62-70,9.DOI:10.19951/j.cnki.1672-9331.20240508002
基于随机森林算法的波浪参数降尺度预报模型
Wave parameter downscaling forecasting model based on random forest algorithm
摘要
Abstract
[Purposes]This paper aims to build accurate and fast wave forecasting models to ensure marine activities and beach safety.[Methods]The wave data and wind speed data in the Chinese Wave Database(CWAVE)from 2009 to 2018 were used as training samples to establish a rapid wave downscaling forecasting model that integrated a wave model with a random forest machine learning algorithm.The numerical wave model implemented calculations on a coarse grid,and wave downscaling forecasting was performed through the random forest algorithm,enabling rapid forecasting of wave elements in nearshore areas.[Findings]A long-term series forecasting of significant wave height,average period,and main wave direction for the entire year of 2019 in the offshore area of the Yangtze River Estuary is conducted.It is found that the established wave downscaling forecasting model can accurately predict the changes in typhoon waves and cold waves throughout the year.The relative error of the significant wave height calculated by the model is within 0.2%compared to the calculation results of the traditional wave model,the calculation efficiency is significantly improved,with the 96-hour short-term wave forecasting advancing from a minute to a second level.[Conclusions]The rapid wave downscaling forecasting model that integrates a wave model with the random forest algorithm can enhance the stability,accuracy,and calculation efficiency of wave forecasting,providing a new method for the operational application of wave machine learning algorithms in wave forecasting.关键词
波浪预报/机器学习/随机森林算法/台风浪/寒潮浪Key words
wave forecasting/machine learning/random forest/typhoon wave/cold wave分类
水利科学引用本文复制引用
王晓惠,施渊,沈旭伟,陈有俊,孙海飞,时健..基于随机森林算法的波浪参数降尺度预报模型[J].长沙理工大学学报（自然科学版）,2025,22(1):62-70,9.基金项目
中国能源建设集团江苏省电力设计院有限公司科技项目(32-JK-2023-026) （32-JK-2023-026）
国家重点研发计划项目(2022YFC3106100) Project(32-JK-2023-026)supported by Science and Technology Project of China Energy Engineering Group Jiangsu Power Design Institute Co.,Ltd.,China Energy Construction Group （2022YFC3106100）
Project(2022YFC3106100)supported by National Key R&D Program of China （2022YFC3106100）
