Predictive explicit expressions from data-driven models for estimation of scour depth below ski-jump bucket spillways

Abstract

Scour depth estimation is an essential factor in water-related engineering problems. Scouring below spillways may endanger a dam's stability and even lead to dam destruction. As a result, it has undesirable environmental effects due to dam failure. Hence, reliable and accurate scour depth estimation below spillways is an exciting topic for researchers. For this purpose, the published and reliable prototype data related to scour depth below ski jump bucket spillways (Ds) was used to develop data-driven models. This study employed two widely used decision tree (DT) methods, including the M5 model tree (M5MT) and the classification and regression tree (CART), and also multivariate adaptive regression splines (MARS) for the estimation of (Ds). The proposed methods provided explicit and clear equations with straightforward applications for estimating scour depth. For the quantitative assessments of the developed formulas, three common statistical metrics, namely root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC), were used. Moreover, comparison results with previous approaches existing in the literature indicated the efficacy of the suggested methods. The obtained results revealed that the MARS technique was the best approach for the estimation of scour depth. HIGHLIGHTS Predictive equations were developed to estimate the scour depth below ski-jump bucket spillways.; White-box data-driven models were evaluated in this study.; The MARS model provided more accurate results when compared to decision tree methods and empirical formulas for scour depth prediction.; Field measurements were used in this study.;

Keywords

• cart
• data-driven models
• mars
• model tree
• scour depth
• spillways