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Performance improvement of the linear muskingum flood routing model using optimization algorithms and data assimilation approaches

Author

Listed:
  • Aryan Salvati

    (University of Tehran)

  • Alireza Moghaddam Nia

    (University of Tehran)

  • Ali Salajegheh

    (University of Tehran)

  • Parham Moradi

    (University of Kurdistan)

  • Yazdan Batmani

    (University of Kurdistan)

  • Shahabeddin Najafi

    (University of Kurdistan)

  • Ataollah Shirzadi

    (University of Kurdistan)

  • Himan Shahabi

    (University of Kurdistan)

  • Akbar Sheikh-Akbari

    (Creative Technologies and Engineering at Leeds Beckett University)

  • Changhyun Jun

    (College of Engineering Chung-Ang University)

  • John J. Clague

    (Simon Fraser University)

Abstract

The Muskingum model is one of the most widely used hydrological methods in flood routing, and calibrating its parameters is an ongoing research challenge. We optimized Muskingum model parameters to accurately simulate hourly output hydrographs of three flood-prone rivers in the Karun watershed, Iran. We evaluated model performance using the correlation coefficient (CC), the ratio of the root-mean-square error to the standard deviation of measured data (PSR), Nash–Sutcliffe efficiency (NSE), and index of agreement (d). The results show that the gray wolf optimization (GWO) algorithm, with CC = 0.99455, PSR = 0.155, NSE = 0.9757, and d = 0.9945, performed better in simulating the flood in the first study area. The Kalman filter (KF) improved these measures by + 0.00516, − 0.1246, + 0.02328, and + 0.00527, respectively. Our findings for the second flood show that the gravitational search algorithm (GSA), with CC = 0.9941, PSR = 0.1669, NSE = 0.9721, and d = 0.9921, performed better than all other algorithms. The Kalman filter enhanced each of the measures by + 0.00178, − 0.0175, + 0.0055 and + 0.0021, respectively. The gravitational search algorithm also performed best in the third flood, with CC = 0.9786, PSR = 0.2604, NSE = 0.9321, and d = 0.9848, and with improvements in accuracy using the Kalman filter of + 0.01081, − 0.0971, + 0.394, and + 0.0078, respectively. We recommend the use of GWO-KF for flood routing studies with flood events of high volumes and hydrograph base times, and use of GSA-KF for studies with flood events of high volumes and hydrograph base times.

Suggested Citation

  • Aryan Salvati & Alireza Moghaddam Nia & Ali Salajegheh & Parham Moradi & Yazdan Batmani & Shahabeddin Najafi & Ataollah Shirzadi & Himan Shahabi & Akbar Sheikh-Akbari & Changhyun Jun & John J. Clague, 2023. "Performance improvement of the linear muskingum flood routing model using optimization algorithms and data assimilation approaches," 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. 118(3), pages 2657-2690, September.
    • Handle: RePEc:spr:nathaz:v:118:y:2023:i:3:d:10.1007_s11069-023-06113-8
    DOI: 10.1007/s11069-023-06113-8
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    References listed on IDEAS

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    1. Zaw Latt, 2015. "Application of Feedforward Artificial Neural Network in Muskingum Flood Routing: a Black-Box Forecasting Approach for a Natural River System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 4995-5014, November.
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    Cited by:

    1. Kemal Saplıoğlu, 2025. "Calibration of Linear Muskingum Model Coefficients and Coefficient Parameters Using Grey Wolf and Particle Swarm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(3), pages 999-1014, February.

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