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

Aryan Salvati, Alireza Moghaddam Nia (), Ali Salajegheh, Parham Moradi, Yazdan Batmani, Shahabeddin Najafi, Ataollah Shirzadi, Himan Shahabi, Akbar Sheikh-Akbari, Changhyun Jun and John J. Clague
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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

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 118, issue 3, No 33, 2657-2690

Abstract: 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.

Keywords: Flood routing; Muskingum model; Artificial intelligence; Data assimilation; Peak flow simulation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-023-06113-8

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