Evolutionary polynomial regression approach to predict longitudinal dispersion coefficient in rivers

The fate of pollutants in rivers is mainly affected by the longitudinal dispersion coefficient (K_x). Thus, improved K_x estimation could greatly enhance the water quality management of rivers. In this regard, evolutionary polynomial regression (EPR) was used to accurately predict K_x in rivers as a function of flow depth, channel width, and average and shear velocities. The predicted K_x by EPR modelling was compared with results obtained by more conventional K_x estimation formulas. Initial data analyses using general linear models of variance revealed that all input variables were statistically significant for K_x estimation. The calibrated EPR model showed good performance with coefficient of determination and root mean square error of 0.82 and 79 m²/s, respectively. This is better that other more conventional estimation methods. Application of sensitivity analysis for the EPR model indicated that channel width, average velocity, shear velocity, and flow depth were the main variables in descending order that affected K_x variability. The introduced EPR estimation model for K_x can be incorporated in one-dimensional water quality models for improved simulation of solute concentration in natural rivers.