IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v29y2015i7p2205-2219.html
   My bibliography  Save this article

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

Author

Listed:
  • 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

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-015-0936-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11269-015-0936-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chunlong Li & Jianzhong Zhou & Shuo Ouyang & Chao Wang & Yi Liu, 2015. "Water Resources Optimal Allocation Based on Large-scale Reservoirs in the Upper Reaches of Yangtze River," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2171-2187, May.
    2. Tao Bai & Lianzhou Wu & Jian-xia Chang & Qiang Huang, 2015. "Multi-Objective Optimal Operation Model of Cascade Reservoirs and Its Application on Water and Sediment Regulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2751-2770, June.
    3. Wang Zhang & Pan Liu & Xizhen Chen & Li Wang & Xueshan Ai & Maoyuan Feng & Dedi Liu & Yuanyuan Liu, 2016. "Optimal Operation of Multi-reservoir Systems Considering Time-lags of Flood Routing," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 523-540, January.
    4. Fang Wan & Wenlin Yuan & Jin Zhou, 2017. "Derivation of Tri-level Programming Model for Multi-Reservoir Optimal Operation in Inter-Basin Transfer-Diversion-Supply Project," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 479-494, January.
    5. Shweta Rathi & Rajesh Gupta & Swapnil Kamble & Aabha Sargaonkar, 2016. "Risk Based Analysis for Contamination Event Selection and Optimal Sensor Placement for Intermittent Water Distribution Network Security," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(8), pages 2671-2685, June.
    6. Aurora Gullotta & Alberto Campisano & Enrico Creaco & Carlo Modica, 2021. "A Simplified Methodology for Optimal Location and Setting of Valves to Improve Equity in Intermittent Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4477-4494, October.
    7. Mohit Agarwal & Gur Mauj Saran Srivastava, 2018. "Genetic Algorithm-Enabled Particle Swarm Optimization (PSOGA)-Based Task Scheduling in Cloud Computing Environment," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1237-1267, July.
    8. Bo Ming & Jian-xia Chang & Qiang Huang & Yi-min Wang & Sheng-zhi Huang, 2015. "Optimal Operation of Multi-Reservoir System Based-On Cuckoo Search Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5671-5687, December.
    9. Jianxia Chang & Yanbin Kan & Yimin Wang & Qiang Huang & Lei Chen, 2017. "Conjunctive Operation of Reservoirs and Ponds Using a Simulation-Optimization Model of Irrigation Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 995-1012, February.
    10. J. Yazdi & A. Moridi, 2018. "Multi-Objective Differential Evolution for Design of Cascade Hydropower Reservoir Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4779-4791, November.
    11. Adam P. Piotrowski & Maciej J. Napiorkowski & Monika Kalinowska & Jaroslaw J. Napiorkowski & Marzena Osuch, 2016. "Are Evolutionary Algorithms Effective in Calibrating Different Artificial Neural Network Types for Streamwater Temperature Prediction?," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1217-1237, February.
    12. Chang, Jianxia & Li, Yunyun & Yuan, Meng & Wang, Yimin, 2017. "Efficiency evaluation of hydropower station operation: A case study of Longyangxia station in the Yellow River, China," Energy, Elsevier, vol. 135(C), pages 23-31.
    13. Mohammad Rezaie-Balf & Zahra Zahmatkesh & Sungwon Kim, 2017. "Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non–Parametric Paradigm vs. Model Classification Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3843-3865, September.
    14. Reza Hajiabadi & Mahdi Zarghami, 2014. "Multi-Objective Reservoir Operation with Sediment Flushing; Case Study of Sefidrud Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5357-5376, December.
    15. Wang Zhang & Pan Liu & Xizhen Chen & Li Wang & Xueshan Ai & Maoyuan Feng & Dedi Liu & Yuanyuan Liu, 2016. "Optimal Operation of Multi-reservoir Systems Considering Time-lags of Flood Routing," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 523-540, January.
    16. Monidipa Das & Soumya K. Ghosh & V. M. Chowdary & A. Saikrishnaveni & R. K. Sharma, 2016. "A Probabilistic Nonlinear Model for Forecasting Daily Water Level in Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 3107-3122, July.
    17. Imad Antoine Ibrahim, 2020. "Legal Implications of the Use of Big Data in the Transboundary Water Context," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1139-1153, February.
    18. Jalal Shiri & Shahaboddin Shamshirband & Ozgur Kisi & Sepideh Karimi & Seyyed M Bateni & Seyed Hossein Hosseini Nezhad & Arsalan Hashemi, 2016. "Prediction of Water-Level in the Urmia Lake Using the Extreme Learning Machine Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5217-5229, November.
    19. Luisa Liucci & Daniela Valigi & Stefano Casadei, 2014. "A New Application of Flow Duration Curve (FDC) in Designing Run-of-River Power Plants," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 881-895, February.
    20. Yuxin Zhu & Jianzhong Zhou & Yongchuan Zhang & Zhiqiang Jiang & Benjun Jia & Wei Fang, 2022. "Optimal Energy Storage Operation Chart and Output Distribution of Cascade Reservoirs Based on Operating Rules Derivation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5751-5766, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:29:y:2015:i:7:p:2205-2219. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.