Using applications of machine learning models for predicting and analyzing scour depth at the submerged weir

Weirs are designed to stabilize rivers, grade control, and raise upstream water levels. The failure of these structures is primarily due to local scour at the structural site. Consequently, an accurate estimate of the likely scour depth at the structure is critical for weir design safety and economy. This study proposes machine learning models for scour depth prediction at submerged weirs by introducing advanced gradient boosting algorithms, namely gradient boosting (GB), categorical boosting (CatBoost), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). A database consisting of 308 cases was collected for model calibration and evaluation. The results demonstrate that the GB algorithm is very accurate, with coefficients of determination of 0.99610 and 0.96222 for the training and testing datasets, respectively. The GB model outperforms other developed models, such as support vector regression, decision tree, and ridge models, in the literature. A sensitivity analysis study has determined that the morphological jump parameter is the most significant factor, whereas the normal flow depth on the equilibrium bed slope is the least significant factor in predicting the d_s under the submerged weir.