Theoretical background and application of numerical modeling to surface water resources

Herrera-Granados, Oscar

doi:10.1016/b978-0-323-91910-4.00019-4

Water Resource Modeling and Computational Technologies

2022

DOI: 10.1016/b978-0-323-91910-4.00019-4

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Theoretical background and application of numerical modeling to surface water resources

Oscar Herrera-Granados

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2025

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Comparing the Efficiency of Particle Swarm and Harmony Search Algorithms in Optimizing the Muskingum–Cunge Model

Ahmadi,

Piri,

Galavi

et al. 2025

Water

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Climate change-induced alterations in monsoon patterns have exacerbated flooding challenges in Balochistan, Iran. This study addresses the urgent need for improved flood prediction methodologies in data-scarce arid regions by integrating the Muskingum–Cunge model with advanced optimization techniques. Particle swarm optimization (PSO) and harmony search (HS) algorithms were applied and compared across eight major rivers in Balochistan, each with distinct hydrological characteristics. A comprehensive multi-metric evaluation framework was developed to assess the performance of these algorithms. The results demonstrate PSO’s superior performance, particularly in complex terrain conditions. For instance, at the Kajou station, PSO improved the Coefficient of Residual Mass (CRM) by 0.01, efficiency (EF) by 0.92, Agreement Index (d) by 0.98, and Normalized Root Mean Square Error (NRMSE) by 0.10 compared to HS. Correlation coefficients ranging from 0.6558 to 0.9645 validate the methodology’s effectiveness in data-scarce environments. This research provides valuable insights into algorithm performance under limited data conditions and offers region-specific parameter optimization guidelines for similar geographical contexts. By advancing flood routing science and providing a validated framework for optimization algorithm selection, this study contributes to improved flood management in regions vulnerable to climate change.

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Comparing the Efficiency of Particle Swarm and Harmony Search Algorithms in Optimizing the Muskingum–Cunge Model

Ahmadi,

Piri,

Galavi

et al. 2025

Water

View full text Add to dashboard Cite

show abstract

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Theoretical background and application of numerical modeling to surface water resources

Cited by 1 publication

References 9 publications

Comparing the Efficiency of Particle Swarm and Harmony Search Algorithms in Optimizing the Muskingum–Cunge Model

Comparing the Efficiency of Particle Swarm and Harmony Search Algorithms in Optimizing the Muskingum–Cunge Model

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