Boosting ensembles for estimation of discharge coefficient and through flow discharge in broad-crested gabion weirs

Gabion weirs are environment-friendly structures widely used for irrigation and drainage network purposes. These structures' hydraulic performance is fundamentally different from solid weirs' due to their porosity and the existence of a through-flow discharge. This paper investigates the reliability and suitability of a number of Machine learning models for estimation of hydraulic performance of gabion weirs. Generally, three different Boosting ensemble models, including Gradient Boosting, XGBoost, and CatBoost, are compared to the well-known Random Forest and a Stacked Regression model, with respect to their accuracy in prediction of the discharge coefficient and through-flow discharge ratio of gabion weirs in free flow conditions. The Bayesian optimization approach is used to fine-tune model hyper-parameters automatically. Recursive feature elimination analysis is also performed to find optimum combination of features for each model. Results indicate that the CatBoost model has outperformed other models in terms of estimating the through flow discharge ratio (Q(in)/Q(t)) with R-2 = 0.982, while both XGBoost and CatBoost models have shown close performance in terms of estimating the discharge coefficient (C-d) with R-2 of CatBoost equal to 0.994 and R-2 of XGBoost equal to 0.992. Weakest results were also produced by Decision tree regressor with R-2 = 0.821 and 0.865 for estimation of C-d and Qin/Qt values.