Predicting longitudinal dispersion coefficient in natural streams using minimax probability machine regression and multivariate adaptive regression spline

This article employs minimax probability machine regression (MPMR) and multivariate adaptive regression spline (MARS) for prediction of longitudinal dispersion coefficient in natural streams. The variables of hydraulic features such as channel width (B), flow depth (H), flow velocity (U), shear velocity (u*) and geometric features such as channel sinuosity (σ) and channel shape parameter (β) were taken as the input. The dispersion coefficient Kxwas the decision parameter for the proposed machine learning models. MARS does not assume any functional relationship between inputs and output. The MARS model is a non-parametric regression model that splits the data and fits each interval into a basis function. MPMR is a probabilistic model which maximises the minimum probability of predicted output. MPMR also provides output within some bound of the true regression function. The proposed study gives an equation for prediction of longitudinal dispersion coefficient based on the developed MARS. The developed MARS has been compared with proposed MPMR. Finally, the performances of the models have been measured by different performance metrics.