Model of Storm Surge Maximum Water Level Increase in a Coastal Area Using Ensemble Machine Learning and Explicable Algorithm.

This study proposes a novel, new ensemble model (NEM) designed to simulate the maximum water level increases caused by storm surges in a frequently cyclone‐affected coastal water of Hong Kong, China. The model relies on storm and water level data spanning 1978–2022. The NEM amalgamates three machine learning algorithms: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and XGBoost (XGB), employing a stacking technique for integration. Six parameters, determined using the Random Forest and Recursive Feature Elimination algorithms (RF‐RFE), are used as input features for the NEM. These parameters are the nearest wind speed, gale distance, nearest air pressure, minimum distance, maximum pressure drop within 24 hr, and large wind radius. Model assessment results suggest that the NEM exhibits superior performance over RF, GBDT, and XGB, delivering high stability and precision. It reaches a coefficient of determination (R2) up to 0.95 and a mean absolute error (MAE) that fluctuates between 0.08 and 0.20 m for the test data set. An interpretability analysis conducted using the SHapley Additive exPlanations (SHAP) method shows that gale distance and nearest wind speed are the most significant features for predicting peak water level increases during storm surges. The results of this study could provide practical implications for predictive models concerning storm surges. These findings present essential tools for the mitigation of coastal disasters and the improvement of marine disaster warning systems. Plain Language Summary: This research introduces an innovative ensemble machine learning model that predicts maximum water level rises due to storm surges in Hong Kong's cyclone‐prone coastlines. The model blends three machine learning algorithms, namely Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and XGBoost (XGB), using storm and surge data from 1978 to 2022. It employs six parameters, including nearest wind speed and gale distance, providing superior performance with high stability and precision. Using SHapley Additive exPlanations (SHAP) analysis, gale distance and wind speed emerged as key predictors. The NEM could enhance storm surge models, aiding in coastal disaster mitigation and improving maritime warning systems. Key Points: An ensemble learning model has been developed to predict the peak water level rise during storm surges in Hong Kong's coastal regionsThe ensemble approach outperforms standalone machine learning methods of Random Forest, Gradient Boosting Decision Tree, and XGBoostAn interpretability analysis reveals that gale distance (Gale_Dis), and the nearest wind speed (N_WS) are the most influential features for storm surge prediction [ABSTRACT FROM AUTHOR]