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Comparative evaluation of parallel optimization algorithms for urban drainage modeling using OSTRICH-SWMM
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DOI：10.3389/frwa.2026.1786203.png)
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Abstract
En 中文
Accurate calibration of urban drainage models is critical for reliable stormwater management. This study applies the OSTRICH-SWMM framework; which integrates the Storm Water Management Model (SWMM) with multiple parallel optimization algorithms; to systematically evaluate calibration performance in a large-scale urban drainage system with a total area of 25.6 km2 located in the southern part of Jiujiang; China. Three algorithms were tested: Asynchronous Parallel Dynamically Dimensioned Search (ParaDDS); Real-coded Genetic Algorithm (RGA); and Simulated Annealing (SA). Results show that ParaDDS delivered the most robust performance; achieving Nash–Sutcliffe efficiency (NSE) values of 0.864 for calibration rainfall event and 0.757 for validation event; with minimal variability among top 25-percentile solutions. RGA also performed well with a NSE value of 0.861 for calibration and 0.601 for validation whereas SA had a NSE value of 0.778 for calibration and 0.482 for validation. These findings demonstrate the effectiveness of the OSTRICH-SWMM framework for automatic calibration of complex urban drainage systems and underscore the capability of parallel optimization algorithms; particularly ParaDDS; in achieving stable and reliable parameter sets.
Keywords:
SWMM
automatic calibration
ParaDDS
parallel optimization algorithm
urban drainage model
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F
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2.8
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360
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2.1K
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