IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v34y2020i2d10.1007_s11269-019-02463-w.html
   My bibliography  Save this article

Receiving More Accurate Predictions for Longitudinal Dispersion Coefficients in Water Pipelines: Training Group Method of Data Handling Using Extreme Learning Machine Conceptions

Author

Listed:
  • Farid Saberi-Movahed

    (Graduate University of Advanced Technology)

  • Mohammad Najafzadeh

    (Graduate University of Advanced Technology)

  • Adel Mehrpooya

    (Shahid Bahonar University of Kerman)

Abstract

Longitudinal dispersion coefficient (LDC) is known as the most remarkable environmental variables which plays a key role in evaluation of pollution profiles in water pipelines. Even though, there is a wide range of numerical models to estimate coefficient of longitudinal dispersion, these mathematical techniques may often come in quite few inaccuracies due to complex mechanism of convection-diffusion processes in pollutant transition in water pipelines. In this research work, to obtain more accurate prediction of LDC, general structure of group method of data handling (GMDH) is modified by means of extreme learning machine (ELM) conceptions. In fact, with getting inspiration from ELM, a novel GMDH method, called GMDH network based on using extreme learning machine (GMDH-ELM), is proposed in which weighting coefficients of quadratic polynomials applied in conventional GMDH are no longer required to be updated either using back propagation technique or other evolutionary algorithms through training stage. In fact, an intermediate parameter is employed to establish a relationship between the input and output in each neuron of the GMDH model. In this way, a well-known and reliable dataset (233 experimental data) related to LDC in water network pipelines, as output vector, is applied to conduct training and testing phases. Through datasets, the Re number, the average longitudinal flow velocity, the friction factor of pipeline and the diameter of pipe are considered as inputs of the proposed approach. The results of GMDH-ELM model indicate a highly satisfying level of precision in both training and testing phases. Furthermore, feed forward structure of GMDH model was improved by particle swarm optimization (PSO) and gravitational search algorithm (GSA) to predict LDC. Through a sound judgment, a comparison is drawn between the performance of GMDH-ELM and other developed GMDH models. Moreover, several empirical equations existing in literature have been applied for comparisons. Overall, results of GMDH-ELM have permissible superiority over the other soft computing tools and conventional predictive models.

Suggested Citation

  • Farid Saberi-Movahed & Mohammad Najafzadeh & Adel Mehrpooya, 2020. "Receiving More Accurate Predictions for Longitudinal Dispersion Coefficients in Water Pipelines: Training Group Method of Data Handling Using Extreme Learning Machine Conceptions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 529-561, January.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:2:d:10.1007_s11269-019-02463-w
    DOI: 10.1007/s11269-019-02463-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-019-02463-w
    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/s11269-019-02463-w?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. Ping He & Tao Tao & Kunlun Xin & Shuping Li & Hexiang Yan, 2016. "Modelling Water Distribution Systems with Deficient Pressure: An Improved Iterative Methodology," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 593-606, January.
    2. Sara Nazif & Mohammad Karamouz & Mohsen Yousefi & Zahra Zahmatkesh, 2013. "Increasing Water Security: An Algorithm to Improve Water Distribution Performance," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2903-2921, June.
    3. Annika Malm & Frida Moberg & Lars Rosén & Thomas Pettersson, 2015. "Cost-Benefit Analysis and Uncertainty Analysis of Water Loss Reduction Measures: Case Study of the Gothenburg Drinking Water Distribution System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5451-5468, December.
    4. Ping He & Tao Tao & Kunlun Xin & Shuping Li & Hexiang Yan, 2016. "Modelling Water Distribution Systems with Deficient Pressure: An Improved Iterative Methodology," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 593-606, January.
    5. De Giorgi, M.G. & Malvoni, M. & Congedo, P.M., 2016. "Comparison of strategies for multi-step ahead photovoltaic power forecasting models based on hybrid group method of data handling networks and least square support vector machine," Energy, Elsevier, vol. 107(C), pages 360-373.
    6. Naserbegi, A. & Aghaie, M. & Minuchehr, A. & Alahyarizadeh, Gh, 2018. "A novel exergy optimization of Bushehr nuclear power plant by gravitational search algorithm (GSA)," Energy, Elsevier, vol. 148(C), pages 373-385.
    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. Kutlu Onay, Funda, 2023. "A novel improved chef-based optimization algorithm with Gaussian random walk-based diffusion process for global optimization and engineering problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 212(C), pages 195-223.
    2. Mohammad Bahrami Yarahmadi & Abbas Parsaie & Mahmood Shafai-Bejestan & Mostafa Heydari & Marzieh Badzanchin, 2023. "Estimation of Manning Roughness Coefficient in Alluvial Rivers with Bed Forms Using Soft Computing Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3563-3584, July.
    3. Umar, Muhammad & Sabir, Zulqurnain & Raja, Muhammad Asif Zahoor & Aguilar, J.F. Gómez & Amin, Fazli & Shoaib, Muhammad, 2021. "Neuro-swarm intelligent computing paradigm for nonlinear HIV infection model with CD4+ T-cells," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 188(C), pages 241-253.
    4. Boudy Bilal & Kaan Yetilmezsoy & Mohammed Ouassaid, 2024. "Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power," Energies, MDPI, vol. 17(3), pages 1-36, February.
    5. Márton Gosztonyi & Csákné Filep Judit, 2022. "Profiling (Non-)Nascent Entrepreneurs in Hungary Based on Machine Learning Approaches," Sustainability, MDPI, vol. 14(6), pages 1-20, March.
    6. Ousmane Diao & P.-A. Absil & Mouhamadou Diallo, 2023. "Generalized Linear Models to Forecast Malaria Incidence in Three Endemic Regions of Senegal," IJERPH, MDPI, vol. 20(13), pages 1-27, July.
    7. Heelak Choi & Sang-Ik Suh & Su-Hee Kim & Eun Jin Han & Seo Jin Ki, 2021. "Assessing the Performance of Deep Learning Algorithms for Short-Term Surface Water Quality Prediction," Sustainability, MDPI, vol. 13(19), pages 1-11, September.
    8. Ahmed M. Ebid & Ahmed Farouk Deifalla & Hisham A. Mahdi, 2022. "Evaluating Shear Strength of Light-Weight and Normal-Weight Concretes through Artificial Intelligence," Sustainability, MDPI, vol. 14(21), pages 1-49, October.

    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. Xiang He & Yongbo Yuan, 2019. "A Framework of Identifying Critical Water Distribution Pipelines from Recovery Resilience," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3691-3706, September.
    2. José Antonio Palomero-González & Vicent Almenar-Llongo & Ramón Fuentes-Pascual, 2021. "Evaluating the Efficiency of Water Distribution Network Sectors Using the DEA-Weight Russell Directional Distance Model: The Case of the City of Valencia (Spain)," Sustainability, MDPI, vol. 13(19), pages 1-21, September.
    3. Hui Zhang & Xin Cheng & Tinglin Huang & Haibing Cong & Jinlan Xu, 2017. "Hydraulic Analysis of Water Distribution Systems Based on Fixed Point Iteration Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(5), pages 1605-1618, March.
    4. Mingyuan Zhang & Juan Zhang & Gang Li & Yuan Zhao, 2020. "A Framework for Identifying the Critical Region in Water Distribution Network for Reinforcement Strategy from Preparation Resilience," Sustainability, MDPI, vol. 12(21), pages 1-17, November.
    5. Tiku T. Tanyimboh & Anna M. Czajkowska, 2021. "Entropy maximizing evolutionary design optimization of water distribution networks under multiple operating conditions," Environment Systems and Decisions, Springer, vol. 41(2), pages 267-285, June.
    6. Agathoklis Agathokleous & Chrystalleni Christodoulou & Symeon E. Christodoulou, 2017. "Topological Robustness and Vulnerability Assessment of Water Distribution Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 4007-4021, September.
    7. Kanwal, S. & Khan, B. & Ali, S.M. & Mehmood, C.A., 2018. "Gaussian process regression based inertia emulation and reserve estimation for grid interfaced photovoltaic system," Renewable Energy, Elsevier, vol. 126(C), pages 865-875.
    8. Shuyu Dai & Dongxiao Niu & Yaru Han, 2018. "Forecasting of Energy-Related CO 2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability," Sustainability, MDPI, vol. 10(4), pages 1-17, March.
    9. Zhou, Yi & Zhou, Nanrun & Gong, Lihua & Jiang, Minlin, 2020. "Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine," Energy, Elsevier, vol. 204(C).
    10. Kaloop, Mosbeh R. & Bardhan, Abidhan & Kardani, Navid & Samui, Pijush & Hu, Jong Wan & Ramzy, Ahmed, 2021. "Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    11. Hassan Tolba Aboelnga & Lars Ribbe & Franz-Bernd Frechen & Jamal Saghir, 2019. "Urban Water Security: Definition and Assessment Framework," Resources, MDPI, vol. 8(4), pages 1-19, November.
    12. Tri Heryanto & Saroj K. Sharma & D. Daniel & Maria Kennedy, 2021. "Estimating the Economic Level of Water Losses (ELWL) in the Water Distribution System of the City of Malang, Indonesia," Sustainability, MDPI, vol. 13(12), pages 1-15, June.
    13. 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.
    14. Rigby, Aidan & Lindley, Ben & Cullen, Jonathan, 2023. "An exergy based assessment of the efficiency of nuclear fuel cycles," Energy, Elsevier, vol. 264(C).
    15. Xu, Lei & Hou, Lei & Zhu, Zhenyu & Li, Yu & Liu, Jiaquan & Lei, Ting & Wu, Xingguang, 2021. "Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm," Energy, Elsevier, vol. 222(C).
    16. Amir Nafi & Jacques Tcheng & Patrick Beau, 2015. "Comprehensive Methodology for Overall Performance Assessment of Water Utilities," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5429-5450, December.
    17. Manoj Verma & Harish Kumar Ghritlahre, 2023. "Forecasting of Wind Speed by Using Three Different Techniques of Prediction Models," Annals of Data Science, Springer, vol. 10(3), pages 679-711, June.
    18. Frangopoulos, Christos A., 2018. "Recent developments and trends in optimization of energy systems," Energy, Elsevier, vol. 164(C), pages 1011-1020.
    19. Moreno, Sinvaldo Rodrigues & dos Santos Coelho, Leandro, 2018. "Wind speed forecasting approach based on Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference System," Renewable Energy, Elsevier, vol. 126(C), pages 736-754.
    20. Deliang Sun & Jianping Wu & Fengtai Zhang & Weici Su & Hong Hui, 2018. "Evaluating Water Resource Security in Karst Areas Using DPSIRM Modeling, Gray Correlation, and Matter–Element Analysis," Sustainability, MDPI, vol. 10(11), pages 1-16, October.

    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:34:y:2020:i:2:d:10.1007_s11269-019-02463-w. 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.