《海洋预报》| 基于XGBoost和SHAP的海滩波浪爬高预测研究

摘要：海滩波浪爬高预测是海岸侵蚀防护和防灾减灾的关键技术支撑。针对现有经验公式在精确度、泛化性等方面的不足，将极限梯度提升模型XGBoost引入到波浪爬高预测中，利用1 400多个来自实验室和现场观测的海滩波浪爬高数据，通过贝叶斯优化进行超参数调整，建立基于XGBo

基于XGBoost和SHAP的海滩波浪爬高预测研究

作者：张建1 2丁佩1 2刘楷操1 2路川藤3

单位：

1. 珠海市规划设计研究院, 广东珠海 519000;

2. 广东省滨海地区防灾减灾工程技术研究中心, 广东珠海 519000;

3. 南京水利科学研究院, 江苏南京 210029

分类号：P731.33

出版年·卷·期（页码）：2025·42·第二期（1-8）

摘要：海滩波浪爬高预测是海岸侵蚀防护和防灾减灾的关键技术支撑。针对现有经验公式在精确度、泛化性等方面的不足，将极限梯度提升模型XGBoost引入到波浪爬高预测中，利用1 400多个来自实验室和现场观测的海滩波浪爬高数据，通过贝叶斯优化进行超参数调整，建立基于XGBoost的海滩波浪爬高预测模型。此外，还将可解释机器学习框架SHAP与XGBoost模型结合，以挖掘波浪爬高预测结果的关键特征。评估结果表明：XGBoost模型的决定系数为0.957，均方根误差为0.384 m，显著优于其他经验公式，整体预测可靠稳定；SHAP分析也表明XGBoost模型的预测趋势符合真实走向，且Iribarren数在海滩波浪爬高预测中起着关键作用。

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

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 laboratory 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.

Key words：machine learning; wave run-up; XGBoost; bayesian optimization; SHAP