文献类型：期刊文献

英文题名：Monthly extreme wave height prediction based on an LSTM-Stacking model

作者：Tan, Sisi[1,2];Li, Zhiqiang[1];Wu, Zeyu[2];Zhu, Daoheng[1];Zhu, Yuliang[2]

机构：[1]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Coll Marine Engn & Energy, Zhanjiang 524088, Peoples R China

年份：2026

卷号：30

外文期刊名：RESULTS IN ENGINEERING

收录：ESCI(收录号：WOS:001739668300001)、EI(收录号：20261420428792)、WOS

基金：This work was supported by the National Natural Science Foundation of China (No. 42176167) ; the Guangdong Basic and Applied Basic Research Foundation (No. 2024A1515011427) , the scientific research start-up funds of Guangdong Ocean University (No.060302112317) , the Science and Technology Project of Zhanjiang City (No.2025B01061) .

语种：英文

外文关键词：Long short-term memory network; Stacking algorithm; Integrated machine learning; Extreme wave height prediction

外文摘要：Extreme wave height is a key parameter in ocean dynamics and a critical indicator for ocean engineering and marine risk assessment. However, existing prediction models often fail to adequately address the nonstationary characteristics of wave height time series. To overcome these limitations, this study proposes an extreme wave height prediction framework that integrates feature engineering with a Long Short-Term Memory (LSTM)-based Stacking ensemble learning approach. Time based and physics-informed features are constructed after missingvalue imputation to form a multidimensional input dataset. LSTM networks are combined with eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), Random Forest (RF), Support Vector Regression (SVR), and K-Nearest Neighbors (KNN) to capture nonlinear patterns and complex dependencies in wave height series, while a Stacking meta-learner is employed to optimize ensemble outputs. A physics-informed constraint calibrator is further introduced to enhance physical consistency and prediction accuracy. The model is validated using measured wave data from National Data Buoy Center (NDBC) station 42002 and evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE). Comparative experiments demonstrate that the proposed LSTM-Stacking model outperforms standalone LSTM, XGB, and conventional Stacking approaches in monthly extreme wave height prediction, providing reliable forecasting support for offshore engineering and marine disaster prevention.