An intelligent evolutionary extreme gradient boosting algorithm development for modeling scour depths under submerged weir

This research presents a new hybridized evolutionary artificial intelligence (AI) model for modeling depth scouring under submerged weir (d(s)). The proposed model is based on the hybridization of the Extreme Gradient Boosting (XGBoost) model and genetic algorithm (GA) optimizer. The GA is hybridized to solve the hyper-parameter problem of the XGBoost model and to recognize the influential input predictors of d(s). The proposed XGBoost-GA model is developed based on the incorporation of fifteen physical parameters of submerged weir. The feasibility of the XGBoost-GA model is validated against several well-established AI models introduced in the literature in addition to a hybrid XGBoost-Grid model. Several statistical performance metrics is computed for the modeling evaluation in parallel with a graphical assessment. Based on the attained prediction results, the proposed model revealed an optimistic and superior predictability performance with a maximum coefficient of determination (R-2 = 0.933) and a minimum root mean square error (RMSE = 0.014 m). In addition, the XGBoost-GA model demonstrated reliable feature selection for the essential physical parameters. The fifteen parameters are re-scaled to seven parameters based on their essential impacts on the d(s) determination. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This study introduces a new hybridized evolutionary artificial intelligence model for modeling depth scouring, based on XGBoost model and genetic algorithm optimizer. The proposed XGBoost-GA model shows optimistic and superior predictability performance, with a maximum coefficient of determination of 0.933 and a minimum root mean square error of 0.014 m. The model also demonstrates reliable feature selection for essential physical parameters impacting the determination of d(s).