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

Scour depth estimation is an essential factor in water-related engineering problems. Scouring below spillways may endanger the 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 (D-s) 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 the multivariate adaptive regression spline (MARS) for the estimation of (D-s). 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 that existed 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.

Automated summary

Estimating scour depth is crucial in water-related engineering, as inaccurate estimation can lead to dam instability and environmental consequences. This study developed data-driven models using published prototype data to estimate scour depth below spillways. The proposed methods were evaluated using statistical metrics and were found to be effective compared to previous approaches.