Developing ensemble models for estimating sediment loads for different times scales

Sediment ratings supply an important input to the design of water resources projects. Nevertheless, the accuracy of sediment ratings has remained a matter of concern for hydrologists. The present article investigates both the aspect of improving the accuracy, i.e., modifying the simple rating curve equation by introducing a four-parameter equation and application of ensemble machine learning (ML) and ensemble empirical models, to estimate sediment loads. The ML models include artificial neural networks, multi-gene genetic programming (MGGP), and a hybrid MGGP-based model. Published field data at two measuring stations were used to assess the performance of different models employed in this study. The comparative analysis conducted in this study provides a novel comparison of sediment load estimations for three time scales. For instance, the ML-based simple average ensemble model (i.e., 556.5, 255.0, and 0.759) and the empirical-based nonlinear ensemble model (i.e., 549.1, 378.6, and 0.589) achieved the lowest root-mean-square errors and mean absolute errors and highest determination coefficients for the train and test monthly sediment data of the first station, respectively. Finally, the findings demonstrate that ensemble-based models generally improve the estimates of sediment loads at daily, 10-daily, and monthly scales.