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Modeling River Mixing Mechanism Using Data Driven Model

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  • Amir Hamzeh Haghiabi

    (Lorestan University)

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 .

Suggested Citation

  • Amir Hamzeh Haghiabi, 2017. "Modeling River Mixing Mechanism Using Data Driven Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 811-824, February.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:3:d:10.1007_s11269-016-1475-7
    DOI: 10.1007/s11269-016-1475-7
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    References listed on IDEAS

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    1. Mohammad Najafzadeh & Ahmed Sattar, 2015. "Neuro-Fuzzy GMDH Approach to Predict Longitudinal Dispersion in Water Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2205-2219, May.
    2. Tao Jiang & Ming Zhong & Ying-jie Cao & Long-jian Zou & Bo Lin & Ai-ping Zhu, 2016. "Simulation of Water Quality under Different Reservoir Regulation Scenarios in the Tidal River," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3593-3607, August.
    3. Omid Bozorg-Haddad & Mahboubeh Zarezadeh-Mehrizi & Mehri Abdi-Dehkordi & Hugo A. Loáiciga & Miguel A. Mariño, 2016. "A self-tuning ANN model for simulation and forecasting of surface flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 2907-2929, July.
    4. Hazi Azamathulla & Aminuddin Ghani, 2011. "Genetic Programming for Predicting Longitudinal Dispersion Coefficients in Streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(6), pages 1537-1544, April.
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    Cited by:

    1. Sadeghi, Gholamabbas & Najafzadeh, Mohammad & Ameri, Mehran, 2020. "Thermal characteristics of evacuated tube solar collectors with coil inside: An experimental study and evolutionary algorithms," Renewable Energy, Elsevier, vol. 151(C), pages 575-588.

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