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

Abstract

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.

Prediction of storm surge in the Pearl River Estuary based on data-driven model

“…Subsequently, they evaluated predictions using various machine learning (ML) models, including Ridge Regression (RR), Support Vector Machines (SVM), Decision Tree (DT), Random Forest (RF), Extra Trees (ET), Gradient Boosted Decision Tree (GBDT), and Adaptive Boost (AdaBoost). Sun and Pan (2023) innovatively integrated three ML algorithms, RF, GBDT, and XGBoost (XGB), to predict storm surge heights at four tide gauge stations along the coast of Hong Kong, achieving higher accuracy and stability. Al Kajbaf and Bensi (2020) compared the performance of storm surge prediction using artificial neural networks (ANN) based on DL, Gaussian process regression (GPR), and SVM based on ML.…”