Predicting maximum scour depth uncertainty downstream of grade control structures: a multi-model approach with Bayesian model averaging

This research proposes a novel multi-model combination approach to overcome the limitations of traditional methods in predicting the scour depth downstream of grade control structures. The study evaluates 26 scenarios using a combination of five individual models and assesses the efficiency of four combination methods. Results indicate that GEP and SVM outperform traditional combination methods. Additionally, the study employs Bayesian Model Average (BMA) to assess the uncertainty originating from individual models. The research concludes by providing predictive equations for GEP and SVM, enabling users to apply these methods for accurate maximum scour depth prediction. In the ideal case, the SVM model had an R2 = 0.91, with an RMSE of 0.84, whereas the GEP model achieved an R2 = 0.94, with an RMSE of 0.67. The proposed multi-model approach is more reliable and accurate for predicting maximum scour depth downstream of GCS.