Kernel ridge regression model for sediment transport in open channel flow

Sediment transport modeling is of primary importance for the determination of channel design velocity in lined channels. This study proposes to model sediment transport in open channel flow using kernel ridge regression (KRR), a nonlinear regression technique formulated in the reproducing kernel Hilbert space. While the naïve kernel regression approach provides high flexibility for modeling purposes, the regularized variant is equipped with an additional mechanism for better generalization capability. In order to better tailor the KRR approach to the sediment transport modeling problem, unlike the conventional KRR approach, in this study the kernel parameter is directly learned from the data via a new gradient descent-based learning mechanism. Moreover, for model construction, a procedure based on Cholesky decomposition and forward-back substitution is applied to improve the computational complexity of the approach. Evaluation of the recommended technique is performed utilizing a large number of laboratory experimental data where the examination of the proposed approach in terms of three statistical performance indices for sediment transport modeling indicates a better performance for the developed model in particle Froude number computation, outperforming the conventional models as well as some other machine learning techniques.