IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v31y2017i3d10.1007_s11269-016-1475-7.html
   My bibliography  Save this article

Modeling River Mixing Mechanism Using Data Driven Model

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-016-1475-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11269-016-1475-7?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. 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.
    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. 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.

    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. Gokmen Tayfur, 2017. "Modern Optimization Methods in Water Resources Planning, Engineering and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3205-3233, August.
    2. E. Fallah-Mehdipour & O. Bozorg Haddad & H. Orouji & M. Mariño, 2013. "Application of Genetic Programming in Stage Hydrograph Routing of Open Channels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3261-3272, July.
    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. Bulent Tutmez & Mehmet Yuceer, 2013. "Regression Kriging Analysis for Longitudinal Dispersion Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3307-3318, July.
    5. Peng Zhang & Rui-Feng Liang & Peng-Xiao Zhao & Qing-Yuan Liu & Yong Li & Kai-Li Wang & Ke-Feng Li & Ying Liu & Peng Wang, 2019. "The Hydraulic Driving Mechanisms of Cyanobacteria Accumulation and the Effects of Flow Pattern on Ecological Restoration in Lake Dianchi Caohai," IJERPH, MDPI, vol. 16(3), pages 1-24, January.
    6. Saif Said & Shadab Ali Khan, 2021. "Remote sensing-based water quality index estimation using data-driven approaches: a case study of the Kali River in Uttar Pradesh, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 18252-18277, December.
    7. H. Azamathulla & Robert Jarrett, 2013. "Use of Gene-Expression Programming to Estimate Manning’s Roughness Coefficient for High Gradient Streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 715-729, February.
    8. Wen-Ping Tsai & Yen-Ming Chiang & Jun-Lin Huang & Fi-John Chang, 2016. "Exploring the Mechanism of Surface and Ground Water through Data-Driven Techniques with Sensitivity Analysis for Water Resources Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4789-4806, October.
    9. Mohamad Javad Alizadeh & Davoud Ahmadyar & Ali Afghantoloee, 2017. "Improvement on the Existing Equations for Predicting Longitudinal Dispersion Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(6), pages 1777-1794, April.
    10. 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.
    11. Majid Niazkar & Nasser Talebbeydokhti & Seied Hosein Afzali, 2019. "Novel Grain and Form Roughness Estimator Scheme Incorporating Artificial Intelligence Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 757-773, January.
    12. Mohammad Aghababaei & Amir Etemad-Shahidi & Ebrahim Jabbari & Milad Taghipour, 2017. "Estimation of Transverse Mixing Coefficient in Straight and Meandering Streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3809-3827, September.
    13. Tongtiegang Zhao & Jianshi Zhao & Xiaohui Lei & Xu Wang & Bisheng Wu, 2017. "Improved Dynamic Programming for Reservoir Flood Control Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2047-2063, May.
    14. 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.
    15. Omid Bozorg-Haddad & Pouria Yari & Mohammad Delpasand & Xuefeng Chu, 2022. "Reservoir operation under influence of the joint uncertainty of inflow and evaporation," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 2914-2940, February.
    16. E. Fallah-Mehdipour & O. Bozorg Haddad & M. Mariño, 2012. "Real-Time Operation of Reservoir System by Genetic Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(14), pages 4091-4103, November.
    17. J Sreekanth & Bithin Datta, 2014. "Stochastic and Robust Multi-Objective Optimal Management of Pumping from Coastal Aquifers Under Parameter Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 2005-2019, May.
    18. Ahmad Attari Ghomi & Ayyub Ansarinejad & Hamid Razaghi & Davood Hafezi & Morteza Barazande, 2014. "A Novel Electric Power Plants Performance Assessment Technique Based on Genetic Programming Approach," Modern Applied Science, Canadian Center of Science and Education, vol. 8(3), pages 1-43, June.
    19. Ali Ahani & Mojtaba Shourian & Peiman Rahimi Rad, 2018. "Performance Assessment of the Linear, Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(2), pages 383-399, January.
    20. Neslihan Seckin & Aytac Guven, 2012. "Estimation of Peak Flood Discharges at Ungauged Sites Across Turkey," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(9), pages 2569-2581, July.

    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:31:y:2017:i:3:d:10.1007_s11269-016-1475-7. 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.