A Novel Approach for Predicting peak flow from Breached Dam: Coupling Monte Carlo Simulation, Hydrodynamic Model, and an Interpretable XGBoost Model

Ali El Bilali () and Abdeslam Taleb
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Ali El Bilali: Hassan II University of Casablanca
Abdeslam Taleb: Hassan II University of Casablanca

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 3, No 11, 1177-1194

Abstract: Abstract Predicting peak flow from dam break is crucial in hydraulic engineering. However, Data availability is a great challenge for developing reliable models. In this study, we attempt to develop a new framework to predict peak flow from breached dams using synthetic and real data. Thus, Monte Carlo method was used to generate synthetic samples of the breach parameters for running HEC-RAS-2D to simulate peak flow. Then, XGBoost, Shapley Additive Explanations, and Local Interpretable Model-agnostic Explanations algorithms were applied to analyze and interpret the influence of the input variables with regard dam breach process. The results revealed that the NSE of the XGBoost model ranged from 0.98 to -0.21. The Surface area of the breach and the height of water at failure were identified as main factors followed by weir coefficient and the formation time of the breach. The volume of water at failure was ranked first factor followed by the breach width when the surface area is not considered. Furthermore, the original data, of 111 real dam break events with known Hw and Vw, was merged with synthetic one, to assess XGBoost and showed a high accuracy with NSE about 0.99 and 0.75 during the training and validation phases, respectively. Using both the real and synthetic data significantly improved the accuracy of the XGBoost model with an increase in NSE by 9% during the validation when using (Vw) as input feature. Overall, this study presents a novel and robust approach for predicting peak flow with limited data, offering valuable insights for effective dam safety management and flood risk mitigation.

Keywords: Machine Learning; Breach; Peak Outflow; HEC-RAS-2D Model; Interpretability (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11269-024-04018-0

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