Prediction of peak discharge from earth–rock dam failures based on Bayesian optimization XGBoost regression algorithm

Accurately predicting the peak discharge at the breach of an earth–rock dam is crucial for formulating downstream flood control measures and risk assessments. This paper established a database of 162 earth–rock dam failure cases. Based on the analysis of earth–rock dam failure processes and paramete...

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Vydáno v:	Natural hazards (Dordrecht) Ročník 121; číslo 12; s. 13961 - 13984
Hlavní autoři:	Wang, Ting, Gao, Panrui, Tian, Zhiwen, Li, Yanlong, Qiu, Wen, Shi, Ning, Xu, Zengguang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.07.2025 Springer Nature B.V
Témata:	Accuracy Algorithms Bayesian analysis Bayesian theory Case studies Civil Engineering Correlation analysis Dam failure Dams Datasets Decision trees Discharge Earth Earth and Environmental Science Earth Sciences Empirical models Environmental Management Failure Failure modes Failures Flood control Flood peak Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Goodness of fit Height Hydrogeology Impact analysis Machine learning Mathematical models Natural Hazards Neural networks Optimization Original Paper Prediction models Probability theory Regression analysis Risk assessment Rock Rocks Database SHAP XGBoost Peak discharge Earth–rock dam
ISSN:	0921-030X, 1573-0840
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Shrnutí:	Accurately predicting the peak discharge at the breach of an earth–rock dam is crucial for formulating downstream flood control measures and risk assessments. This paper established a database of 162 earth–rock dam failure cases. Based on the analysis of earth–rock dam failure processes and parameter correlation analysis, we selected dam type, failure mode, the height of water above breach invert, and volume of water stored above breach invert at failure as the control variables. A prediction model of the peak discharge from the earth–rock dam failures is established using the Bayesian optimization (BO) XGBoost algorithm. To show the superiority of the BO-XGBoost regression prediction model, we also selected three additional algorithms including Decision Tree, Random Forest, and Gradient Boosting Decision Tree to establish the prediction models. These models were compared against the BO-XGBoost model and empirical models. The results indicate that the BO-XGBoost model has a maximum goodness of fit R 2 of 0.98, indicating that the proposed model possesses higher predictive accuracy. To analyze the impact of each control variable on the prediction values, the BO-XGBoost model is combined with the interpretable machine learning Shapley Additive exPlanations theory. The global analysis indicates that the volume of water stored above the breach invert at failure has the most significant impact on the peak discharge, followed by the height of water above the breach invert at failure. Finally, the feasibility and application of the BO-XGBoost model are further verified by analyzing the typical cases of the Shimantan dam and the Teton dam.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	0921-030X 1573-0840
DOI:	10.1007/s11069-025-07338-5