Improving one-dimensional pollution dispersion modeling in rivers using ANFIS and ANN-based GA optimized models

Simulation and prediction of the pollution transport is one of the major problems in environmental and rivers engineering studies. The numerical tools have been used in simulation of the concentration profile transmission for description of river water quality. The one-dimensional advection-dispersion equation (ADE) is used in applied water quality modeling and requires the accurate estimation of longitudinal dispersion coefficient (D_x). This paper develops a hybrid numerical-intelligence model for dispersion modeling in open-channel flows. The main contribution of this paper is to improve the results of 1D numerical simulation of pollutant transport in steady flows by estimation of dispersion coefficient (D_x) based on artificial intelligence models and subset selection of maximum dissimilarity (SSMD). The developed hybrid model uses an intelligence module based on optimized adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANNs) for longitudinal dispersion estimation, in which their structures are optimized by genetic algorithm (GA). Intelligence estimates of D_x by ANN, ANFIS, ANFIS-GA, ANN-GA, multiple linear regression (MLR), and empirical equation are compared with observed values of D_x available in 505 river section, and the ANFIS-GA, as the most accurate, is incorporated and integrated with developed 1D-ADE numerical module. The numerical solution of 1D-ADE is done using physically influenced scheme (PIS) for face flux estimation in finite volume method. The performance of hybrid models PIS-ANFIS-GA, PIS-ANFIS, and PIS-empirical is compared using the R², RMSE, MAE, and NSE values in comparison with analytical solution and measured concentration hydrographs. The results revealed that the hybrid numerical-intelligence model is more accurate than the other classical methods for sediment/pollutant dispersion prediction in open-channel flows. The developed hybrid numerical-intelligence model can accurately simulate the dispersion processes in rivers and is a novel step in applicability of ANFIS-GA and ANN-GA models.

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