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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Vydané v:	Natural hazards (Dordrecht) Ročník 121; číslo 12; s. 13961 - 13984
Hlavní autori:	Wang, Ting, Gao, Panrui, Tian, Zhiwen, Li, Yanlong, Qiu, Wen, Shi, Ning, Xu, Zengguang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Dordrecht Springer Netherlands 01.07.2025 Springer Nature B.V
Predmet:	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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