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Neuro-Fuzzy GMDH Approach to Predict Longitudinal Dispersion in Water Networks

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  • Mohammad Najafzadeh
  • Ahmed Sattar

Abstract

Longitudinal dispersion in pipelines leads to changes in the characteristics of contaminants. It is critical to quantify these changes because the contaminants travel through water networks or through chemical reactors. The essential characteristics of longitudinal dispersion in pipes can be described by the longitudinal dispersion coefficient. This paper presents the application of the adaptive Neuro fuzzy group method of data handling to develop new empirical formulae for the prediction of longitudinal dispersion coefficients in pipe flow using 233 experimental case studies of dispersion coefficient with a R e range of 900 to 500,000 spanning laminar, transitional and turbulent pipe flow. The NF-GMDH network was improved using particle swarm optimization based evolutionary algorithm. The group method data handling is used to develop empirical relations between the longitudinal dispersion coefficient and various control variables, including the Reynolds number, the average velocity, the pipe friction coefficient and the pipe diameter. GMDH holds advantage in the case of small data samples due to the optimal choice of the model complexity with automatic adaptation to an unknown level of the data uncertainties. Sensitivity analysis is performed on the developed model and the weight and importance of each control variable is presented. The results indicate that the proposed relations are simpler than previous numerical solutions and can effectively evaluate the longitudinal dispersion coefficients in pipe flow. Copyright Springer Science+Business Media Dordrecht 2015

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  • 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.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:7:p:2205-2219
    DOI: 10.1007/s11269-015-0936-8
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    References listed on IDEAS

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    1. Meral Buyukyildiz & Gulay Tezel & Volkan Yilmaz, 2014. "Estimation of the Change in Lake Water Level by Artificial Intelligence Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4747-4763, October.
    2. Jian-xia Chang & Tao Bai & Qiang Huang & Da-wen Yang, 2013. "Optimization of Water Resources Utilization by PSO-GA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3525-3540, August.
    3. Sanjeeb Mohapatra & Aabha Sargaonkar & Pawan Labhasetwar, 2014. "Distribution Network Assessment using EPANET for Intermittent and Continuous Water Supply," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3745-3759, September.
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    Cited by:

    1. 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.
    2. Hassan Sharafi & Isa Ebtehaj & Hossein Bonakdari & Amir Hossein Zaji, 2016. "Design of a support vector machine with different kernel functions to predict scour depth around bridge piers," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(3), pages 2145-2162, December.
    3. Ahmed M. A. Sattar & B. Gharabaghi & Edward A. McBean, 2016. "Prediction of Timing of Watermain Failure Using Gene Expression Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1635-1651, March.
    4. Ahmed Sattar & B. Gharabaghi & Edward McBean, 2016. "Prediction of Timing of Watermain Failure Using Gene Expression Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1635-1651, March.

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