Application of a modern multi-level ensemble approach for the estimation of critical shear stress in cohesive sediment mixture

Exploration of incipient motion study is significantly important for the river hydraulics community. The present study, along with experimental investigation, considered a new multi-level ensemble machine learning (ML) to determine critical shear stress (CSS) of gravel particles in a cohesive mixture of clay-silt-gravel, clay-silt-sand gravel, and clay-sand-gravel. The multi-level ensemble ML included a voting-based ensemble meta-estimator integrated with three modern standalone ensemble techniques, namely extreme gradient boosting (XGBoost), Adaptive boosting (Adaboost), and Random Forest (RF), and performance is compared with three standalone ensemble models for prediction of CSS values. Besides, the optimum input combinations were explored using the forward stepwise selection method, as a correlation-based feature selection, and mutual information theory. The outcomes of simulation indicated that the multi-level ensemble machine learning (voting) model in terms of correlation coefficient (R = 0.9641), and root mean square error (RMSE = 0.2022) was superior to the standalone ensemble techniques, i.e., XGBoost (R = 0.9482, and RMSE = 0.2375), Adaboost (R = 0.9496, and RMSE = 0.2387), and RF (R = 0.9392, and RMSE = 0.2739) for accurate estimation of CSS.