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August 28, 2026Canadian Journal of Civil Engineering

Development of Categorical Boosting Regression on Peak Discharge from Earth–Rock Dam Failures Assessment

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Authors

QHQing HaiLYLiangliang YuNLNa Li

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Overview

Predictive modeling study demonstrates high accuracy of optimized categorical boosting regression for peak breach discharge in earth-rock dams, highlighting improved computational flood risk...

Key Points

  • Develop and evaluate a hyperparameter-optimized Categorical Boosting Regression framework to accurately predict peak breach discharge from earth-rock dam failures for flood risk mitigation.
  • Assembled a database of 162 dam failure cases and applied interquartile range screening to remove 3 outliers, yielding 159 cases split into training (80%) and testing (20%) sets.
  • Trained Categorical Boosting Regression models using dam type, failure mode, dam height, water height above breach invert, and stored water volume above breach.
  • Optimized model hyperparameters using two metaheuristic algorithms: Starfish Optimization (StFO) and Tyrannosaurus Rex Optimization (TyRO).
  • The Starfish Optimization variant (CBRStFO) achieved superior performance over CBRTyRO, recording an R² of 0.979 on the training set and 0.967 on the testing set.
  • CBRStFO delivered an overall prediction improvement of approximately 4% relative to CBRTyRO, indicating enhanced reliability for dam failure assessments.

Cite This Study

Hai et al. (2026) studied this question.

synapsesocial.com/papers/6a91466cd15324a1df3aa09fhttps://doi.org/10.1139/cjce-2025-0594
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