Application of Machine Learning Approaches in Particle Tracking Model to Estimate Sediment Transport in Natural Streams

Abstract

Several empirical equations and machine learning approaches have been developed to predict dispersion coefficients in open channels; however, the ability of some learning-based models to predict these coefficients has not yet been evaluated, and the direct application of machine learning-based dispersion coefficients to Lagrangian sediment transport models has not been studied. In this research, data from previous studies is used to evaluate the ability of ensemble machine learning models, i.e., random forest regression (RFR) and gradient boosting regression (GBR), to predict longitudinal and transverse dispersion in natural streams. The optimal principal parameters of ensemble models were adjusted using the grid-search cross-validation technique, and the machine learning-based dispersion models were integrated with a Lagrangian particle tracking model to simulate suspended sediment concentration in natural streams. The resulting suspended sediment concentration distribution was compared with the field data. The results showed that GBR model, with a coefficient of determination (R 2 ) of 0.95, performed better than the RFR model, with R 2 =0.9, in predicting the longitudinal dispersion coefficients in a natural stream in both training and testing stages. However, the RFR model with R 2 = 0.94 performed better than the GBR (R 2 = 0.91) in predicting the transverse dispersion in testing stage. Both models underestimated the dispersion coefficients in the training and testing stages. Comparison between the PTM with ensemble dispersion coefficients and empirical-based dispersion relationships revealed the better performance of the GBR model compared to the other two methods.