文献类型：期刊文献

中文题名：融合MIC与Res-LSTM模型的有效波高预测

英文题名：Fusing MIC and Res-LSTM models for significant wave height prediction

作者：朱道恒[1];李彦[2];李志强[1];刘润[3]

机构：[1]电子与信息工程学院,广东海洋大学,广东湛江524088;[2]大数据与信息工程学院,贵州大学,贵州贵阳550025;[3]化学与环境学院,广东海洋大学,广东湛江524088

年份：2024

卷号：43

期号：4

起止页码：76

中文期刊名：热带海洋学报

外文期刊名：Journal of Tropical Oceanography

收录：北大核心2023、CSTPCD、、CSCD2023_2024、北大核心、CSCD

基金：国家自然科学基金项目(42176167);广东海洋大学科研启动经费项目(060302112317)。

语种：中文

中文关键词：波高预测;最大信息系数;残差网络;长短期记忆网络;支持向量回归

外文关键词：wave height prediction;maximum information coefficient;residual network;long and short-term memory network;support vector regression

中文摘要：有效波高(significant wave height,SWH)的预测在海洋运输和海上活动方面发挥着重要作用。基于中国阳江海陵岛近岸实测数据,提出一种融合最大信息系数(maximal information coefficient,MIC)、残差网络(residual network,ResNet)和长短期记忆网络(long short-term memory networks,LSTM)的预测模型。首先,采用MIC算法从数据集中筛选出与预测指标相关性高的参数作为模型的输入;然后将ResNet引入LSTM中,构建Res-LSTM预测模型;最后选择相关系数(r-squared,R2)、均方根差(root mean squared error,RMSE)、平均绝对误差(mean absolute error,MAE)和平均绝对百分比误差(mean absolute percentage error,MAPE)来评价预测结果。同时,对比了XGBoost(extreme gradient boosting)、SVR(support vector regression)和LSTM网络的预测效果。结果表明,MIC-Res-LSTM模型能够提高短时有效波高预测值的精度。

外文摘要：The prediction of significant wave height(SWH)plays an important role in marine transportation and maritime activities.Based on the near-shore real measurement data of the Hailing Island,Yangjiang,China,a network model integrating the maximum information coefficient algorithm(MIC),residual network(ResNet)and long and short-term memory network(LSTM)is proposed.Firstly,the MIC algorithm was used to screen out the parameters with high correlation with the target predictors from the dataset as the input of the model.Then the residual network was introduced into the LSTM to construct the Res-LSTM prediction model.Finally,the r-squared(R2),root mean square error(RMSE),mean absolute error(MAE)and mean absolute percentage error(MAPE)were selected to evaluate the prediction results.Meanwhile,the prediction results of extreme gradient boosting(XGBoost)network,support vector regression(SVR)network and LSTM network were compared.The results demonstrate that the MIC-Res-LSTM model can improve the accuracy of the short-time significant wave height prediction values.