Comparative evaluation of parallel optimization algorithms for urban drainage modeling using OSTRICH-SWMM

Zia Ul Hassan, Jiaping Su, Dianchang Wang, Lihua Tang, Yukun Hou, Wei Huang, Jun Zhang, Zhenduo Zhu

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 km 2 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.