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Automating HEC-RAS and Linking with Particle Swarm Optimizer to Calibrate Manning’s Roughness Coefficient

Author

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  • Kazem Shahverdi

    (Bu-Ali Sina University
    Hamedan Regional Water Authority)

  • Hossein Talebmorad

    (Hamedan Regional Water Authority
    Isfahan University of Technology)

Abstract

Hydraulic models have a substantial role in the simulation of rivers due to their high accuracy and low cost. One of the most practical hydraulic models is HEC-RAS capable of simulating all flow conditions in watercourses. Floods occurring in rivers are highly dependent on Manning's Roughness Coefficient (MRC). Its optimization, calibration, and uncertainty analysis are necessary. To this end, HEC-RAS should be automated and linked by optimization models so that it can seek to find the optimal values ​​using an iterative process. In this research, HEC-RAS was automated in MATLAB2019 and linked with Particle Swarm Optimization (PSO) and Mont Carlo Simulation (MCS). The sensitivity analysis of PSO was performed, and its optimal coefficients were determined. The MRC calibration was done in the Shahab River in Hamadan province (Iran). The results showed that the MRC in the three distinguished reaches (from upstream to downstream) were respectively obtained as 0.061, 0.057, and 0.040 in the main channel and 0.069, 0.059, and 0.046 in the floodplain. Comparing the obtained values from optimization and estimated values by traditional methods revealed that the optimal values are lower than the estimated ones. The results of the uncertainty analysis of six hydraulic parameters showed that the uncertainty of the velocity is higher than the others. According to the results, the uncertainty is high, therefore, it is recommended MRC is determined with sufficient accuracy to reduce the financial costs and human losses caused by floods.

Suggested Citation

  • Kazem Shahverdi & Hossein Talebmorad, 2023. "Automating HEC-RAS and Linking with Particle Swarm Optimizer to Calibrate Manning’s Roughness Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 975-993, January.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:2:d:10.1007_s11269-022-03422-8
    DOI: 10.1007/s11269-022-03422-8
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    References listed on IDEAS

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    1. Maurice Clerc, 2010. "Beyond Standard Particle Swarm Optimisation," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 1(4), pages 46-61, October.
    2. 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.
    3. Sajjad M. Vatanchi & Mahmoud F. Maghrebi, 2019. "Uncertainty in Rating-Curves Due to Manning Roughness Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(15), pages 5153-5167, December.
    4. Hadi Norouzi & Jalal Bazargan, 2022. "Calculation of Water Depth during Flood in Rivers using Linear Muskingum Method and Particle Swarm Optimization (PSO) Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4343-4361, September.
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    Cited by:

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