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

摘要

Because pollutant dispersion in rivers is strongly influenced by the longitudinal dispersion coefficient (K-x), 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 K-x. Because GC is a black-box model that is not user friendly, an appropriate nonlinear regression (NLR) method was also applied to precisely predict K-x. The inclusion of the generally ignored parameter of river curvature in K-x estimation significantly improved NLR model performance. In so doing, both GC and NLR model estimations of K-x achieved high linear coefficients of determination (R-2) 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 R-2, RMSE, and MAE values equal to 0.997, 8.11, and 2.18, respectively), outperformed the NLR model, particularly for extreme high values of K-x. Similarly to previous studies, it was also found that the most effective parameters on K-x were the channel aspect ratio, friction term, and river curvature, respectively, in descending order of importance. Moreover, a comparison between some well-known K-x 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 K-x prediction. (c) 2017 American Society of Civil Engineers.