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

Author

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  • 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
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    References listed on IDEAS

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