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

• Published in: Information Sciences
• Main Author: Tao H.; Habib M.; Aljarah I.; Faris H.; Afan H.A.; Yaseen Z.M.
• Format: Article
• Language: English
• Published: Elsevier Inc. 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104927523&doi=10.1016%2fj.ins.2021.04.063&partnerID=40&md5=9e89f43955f14004df477278b14a77bb

This research presents a new hybridized evolutionary artificial intelligence (AI) model for modeling depth scouring under submerged weir (ds). 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 ds. 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 (R2 = 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 ds determination. © 2021 Elsevier Inc.