Standalone and Hybrid machine learning approaches to predict sediment load in an alluvial channel

A significant amount of sediment transported in an alluvial river can alter the morphology and shape of the river. Accurate prediction of sediment load is essential in studying the change in geomorphology and dynamics of rivers and also to evaluate its impact on aquatic ecosystems, infrastructure, and human activities dependent on water resources. The present study demonstrates framework for predicting sediment load in alluvial channels using both standalone and hybrid machine learning (ML) models. Multiple datasets collected from various river surveys and flume studies were used to evaluate the significance of key variables such as friction slope (Sf), channel discharge (Q), and bed shear stress (τ_b) affecting the sediment transport employing ML models (Bagging (BA), Random Committee (RC)) and the standalone ML models (Multi-Layer Perceptron Regression (MLPR) and Reduced Error Pruning Tree (REPT). The hybrid Bagging-REPT (BA-REPT) model outperformed other models with a Nash-Sutcliffe Efficiency (NSE) of 0.915, followed by RC-REPT (NSE = 0.906). Among the various variables, friction slope (S_f) was identified as the most influential variable affecting sediment transport behavior. It was also observed that Hybrid models can predict sediment transport behavior more accurately as compared to standalone models and empirical equations. The findings of the study thus demonstrate the importance of hybrid learning in addressing the nonlinear complexity of sediment transport processes.