IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v38y2024i1d10.1007_s11269-023-03672-0.html
   My bibliography  Save this article

Enhancing the EPANET Hydraulic Model through Genetic Algorithm Optimization of Pipe Roughness Coefficients

Author

Listed:
  • Chia-Cheng Shiu

    (National Central University)

  • Chih-Chung Chung

    (National Central University)

  • Tzuping Chiang

    (National Quemoy University)

Abstract

Calibrating hydraulic models for water distribution systems (WDS) is crucial during model-building, particularly in determining the roughness coefficients of pipes. However, using a single roughness coefficient based solely on pipe material can lead to significant variations in frictional head losses. To address this issue and enhance computational efficiency, a genetic algorithm (GA) for optimizing roughness coefficients is presented With the Environmental Protection Agency Network Evaluation Tool (EPANET) hydraulic model. EPANET-GA further considers the spatial characteristics of pipes. We incorporated an automated calibration process and a user graphic interface to analyze the water head pressures of WDS nodes for the Zhonghe-Yonghe Division. The results reveal that the optimized roughness coefficient produces a high correlation coefficient (0.90) with the measured data in a time slot. In addition, a low standard error (8.93%) was achieved for 24-hour predictions. Furthermore, in the Shelin-Beitou Division, spatial characteristics were incorporated as constraints during the calibration process. The EPANET-GA has the potential to serve as an excellent tool for designing, operating, and optimizing water supply networks. It can become an advanced operational solution for administrations, aiding in tasks such as leakage detection and pump energy optimization.

Suggested Citation

  • Chia-Cheng Shiu & Chih-Chung Chung & Tzuping Chiang, 2024. "Enhancing the EPANET Hydraulic Model through Genetic Algorithm Optimization of Pipe Roughness Coefficients," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 323-341, January.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:1:d:10.1007_s11269-023-03672-0
    DOI: 10.1007/s11269-023-03672-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-023-03672-0
    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/s11269-023-03672-0?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. Kun Ding & Yong Ni & Lingfeng Fan & Tian-Le Sun & Tabasam Rashid, 2022. "Optimal Design of Water Supply Network Based on Adaptive Penalty Function and Improved Genetic Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, March.
    2. Chan-Wook Lee & Do-Guen Yoo, 2021. "Development of Leakage Detection Model and Its Application for Water Distribution Networks Using RNN-LSTM," Sustainability, MDPI, vol. 13(16), pages 1-15, August.
    3. Yihong Guan & Mou Lv & Shen Dong, 2023. "Pressure-driven Background Leakage Models and their Application for Leak Localization Using a Multi-population Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 359-373, January.
    4. Mehdi Dini & Massoud Tabesh, 2014. "A New Method for Simultaneous Calibration of Demand Pattern and Hazen-Williams Coefficients in Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 2021-2034, May.
    5. 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.
    6. Gustavo Meirelles & Daniel Manzi & Bruno Brentan & Thaisa Goulart & Edevar Luvizotto, 2017. "Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(13), pages 4339-4351, October.
    7. Tianwei Mu & Yaqi Li & Ziyi Li & Luyue Wang & Haoqiang Tan & Chengzhi Zheng, 2021. "Improved Network Reliability Optimization Model with Head Loss for Water Distribution System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(7), pages 2101-2114, May.
    Full references (including those not matched with items on IDEAS)

    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. Shipeng Chu & Tuqiao Zhang & Chengna Xu & Tingchao Yu & Yu Shao, 2021. "Dealing with Data Missing and Outlier to Calibrate Nodal Water Demands in 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(9), pages 2863-2878, July.
    2. Zheng, Xuejing & Shi, Zhiyuan & Wang, Yaran & Zhang, Huan & Tang, Zhiyun, 2024. "Digital twin modeling for district heating network based on hydraulic resistance identification and heat load prediction," Energy, Elsevier, vol. 288(C).
    3. Yanbo Feng & Han Zhu & Xiwen Feng & Qianru Chen & Xiangyu Sun & Zhengrong Li, 2023. "Optimization of Dual-Design Operation Ventilation System Network Based on Improved Genetic Algorithm," Energies, MDPI, vol. 16(24), pages 1-15, December.
    4. Alemtsehay G. Seyoum & Tiku T. Tanyimboh, 2016. "Investigation into the Pressure-Driven Extension of the EPANET Hydraulic Simulation Model for Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5351-5367, November.
    5. Hyeong-Suk Kim & Dooyong Choi & Do-Guen Yoo & Kyoung-Pil Kim, 2022. "Hyperparameter Sensitivity Analysis of Deep Learning-Based Pipe Burst Detection Model for Multiregional Water Supply Networks," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
    6. Hyeong-Suk Kim & Dooyong Choi & Do-Guen Yoo & Kyoung-Pil Kim, 2022. "Development of the Methodology for Pipe Burst Detection in Multi-Regional Water Supply Networks Using Sensor Network Maps and Deep Neural Networks," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    7. Mehdi Dini & Asghar Asadi, 2020. "Optimal Operational Scheduling of Available Partially Closed Valves for Pressure Management in Water Distribution Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(8), pages 2571-2583, June.
    8. Gustavo Meirelles & Daniel Manzi & Bruno Brentan & Thaisa Goulart & Edevar Luvizotto, 2017. "Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(13), pages 4339-4351, October.
    9. Pham Duc Dai, 2023. "A Real Time Optimization Based Sequential Convex Program for Pressure Management in Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(12), pages 4751-4768, September.
    10. Xin Zhou & Qiquan Ran, 2023. "Optimization of Fracturing Parameters by Modified Genetic Algorithm in Shale Gas Reservoir," Energies, MDPI, vol. 16(6), pages 1-13, March.
    11. Jian Wang & Bangwu Deng & Shuai Jiang & Zhuozhi Deng & Wei Wang & Guowei Chen & Li Liu, 2024. "Optimizing Control of Secondary Water Supply Tanks for Demand Peak Staggering," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(11), pages 4123-4136, September.
    12. Erfan Hajibandeh & Sara Nazif, 2018. "Pressure Zoning Approach for Leak Detection in Water Distribution Systems Based on a Multi Objective Ant Colony Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2287-2300, May.
    13. Majid Niazkar, 2020. "Discussion of “Accurate and Efficient Explicit Approximations of the Colebrook Flow Friction Equation Based on the Wright ω-Function” by Dejan Brkić and Pavel Praks, Mathematics 2019, 7 , 34; doi:10.3," Mathematics, MDPI, vol. 8(5), pages 1-6, May.
    14. Cristiana Bragalli & Matteo Fortini & Ezio Todini, 2016. "Enhancing Knowledge in Water Distribution Networks via Data Assimilation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3689-3706, September.
    15. Ram Kailash Prasad, 2021. "Identification of Critical Pipes for Water Distribution Network Rehabilitation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5187-5204, December.
    16. Ryul Kim & Young Hwan Choi, 2023. "The Development of a Data-Based Leakage Pinpoint Detection Technique for Water Distribution Systems," Mathematics, MDPI, vol. 11(9), pages 1-18, May.
    17. Shipeng Chu & Tuqiao Zhang & Xinhong Zhou & Tingchao Yu & Yu Shao, 2022. "An Efficient Approach for Nodal Water Demand Estimation in Large-scale Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 491-505, January.
    18. Roya Peirovi Minaee & Mehdi Mokhtari & Alireza Moghaddam & Ali Asghar Ebrahimi & Mohsen Askarishahi & Mojtaba Afsharnia, 2019. "Wall Decay Coefficient Estimation in a Real-Life Drinking Water Distribution Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1557-1569, March.
    19. Sanghoon Jun & Kevin E. Lansey, 2023. "Convolutional Neural Network for Burst Detection in Smart Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3729-3743, July.
    20. 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.

    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:38:y:2024:i:1:d:10.1007_s11269-023-03672-0. 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.