Estimation of the Dispersion Coefficient in Natural Rivers Using a Granular Computing Model

Because pollutant dispersion in rivers is strongly influenced by the longitudinal dispersion coefficient (Kx), its accurate estimation is critical in the field of environmentally sound hydraulic engineering. In this study, a granular computing (GC) model was explored for the first time to overcome problems in accurately estimating Kx. Because GC is a black-box model that is not user friendly, an appropriate nonlinear regression (NLR) method was also applied to precisely predict Kx. The inclusion of the generally ignored parameter of river curvature in Kx estimation significantly improved NLR model performance. In so doing, both GC and NLR model estimations of Kx achieved high linear coefficients of determination (R2) and small error indices [root mean square error (RMSE) and mean absolute error (MAE)] with respect to measured Kx values. The same analysis showed that the GC model (with R2, RMSE, and MAE values equal to 0.997, 8.11, and 2.18, respectively), outperformed the NLR model, particularly for extreme high values of Kx. Similarly to previous studies, it was also found that the most effective parameters on Kx were the channel aspect ratio, friction term, and river curvature, respectively, in descending order of importance. Moreover, a comparison between some well-known Kx models and the developed GC and NLR alternative presented here showed the latter to have outperformed the former, indicating that the GC and NLR models are a good choice for Kx prediction.