海洋预报2025，Vol.42Issue(2)：1-8,8.DOI:10.11737/j.issn.1003-0239.2025.02.001
基于XGBoost和SHAP的海滩波浪爬高预测研究
A study on beach wave run-up prediction based on XGBoost and SHAP
摘要
Abstract
Beach wave run-up prediction is a key technical support for coastal erosion protection,disaster prevention and mitigation.In view of the shortcomings of the existing empirical formulas in terms of accuracy and generalization,the XGBoost model is introduced into wave run-up prediction,and more than 1 400 labora-tory and field observations of beach wave run-up are used as a dataset,and hyperparameter tuning is carried out by using Bayesian optimization,which in turn establishes an XGBoost-based wave run-up prediction model.The XGBoost model is used to predict beach wave height,and SHAP,an interpretable machine learning framework,is combined with the XGBoost model to explore the key features of the wave height prediction results.The evaluation results show that the R-squared of the XGBoost model is 0.957,and the root-mean-square error is 0.384 m,which is significantly better than other empirical formulas,and the overall prediction is reliable and stable,meanwhile SHAP shows that the XGBoost model predicted trend is in line with the true value direction and Iribarren number plays a key role in beach wave run-up prediction.关键词
机器学习/波浪爬高/极限梯度提升模型/贝叶斯优化/可解释机器学习框架Key words
machine learning/wave run-up/XGBoost/bayesian optimization/SHAP分类
海洋学引用本文复制引用
张建,丁佩,刘楷操,路川藤..基于XGBoost和SHAP的海滩波浪爬高预测研究[J].海洋预报,2025,42(2):1-8,8.基金项目
水利部重大科技项目(SKS-2022087). （SKS-2022087）
