Modeling River Mixing Mechanism Using Data Driven Model

Amir Hamzeh Haghiabi ()
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Amir Hamzeh Haghiabi: Lorestan University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2017, vol. 31, issue 3, No 6, 824 pages

Abstract: Abstract Modeling river mixing mechanism in terms of pollution transmission in rivers is an important subject in environmental studies. Dispersion coefficient is an important parameter in river mixing problem. In this study, to model and predict the longitudinal dispersion coefficient (D L ) in natural streams, two soft computing techniques including multivariate adaptive regression splines (MARS) as a new approach to study hydrologic phenomena and multi-layer perceptron neural network as a common type of neural network model were prepared. To this end, related dataset were collected from literature and used for developing them. Performance of MARS model was compared with MLP and the empirical formula was proposed to calculate D L . To define the most effective parameters on D L structure of obtained formula from MARS model and more accurate formula was evaluated. Calculation of error indices including coefficient of determination (R2) and root mean square error (RMSE) for the results of MARS model showed that MARS model with R2 = 0.98 and RMSE = 0.89 in testing stage has suitable performance for modeling D L . Comparing the performance of empirical formulas, ANN and MARS showed that MARS model is more accurate compared to others. Attention to the structure of developed MARS and the most accurate empirical formulas model showed that flow velocity, depth of flow (H) and shear velocity are the most influential parameters on D L .

Keywords: Artificial neural network; Longitudinal dispersion coefficient; Pollution transmission (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11269-016-1475-7

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