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<p>Uncontrolled urban runoff can pose significant challenges to infrastructure and private property. While the implementation of Low-Impact Development (LID) strategies has begun to address these issues, designing LID configurations that effectively reduce runoff while simultaneously minimizing costs is a critical task that requires careful consideration. This research identifies the optimal configuration of Low-Impact Development (LID) practices for Sepahan Shahr, Isfahan, Iran, through a hybrid simulation-optimization framework. To mitigate urban runoff across three sub-catchments, bioretention, permeable pavement, and vegetated swales are evaluated. A core novelty of this study lies in the comparative performance analysis of three distinct metaheuristic algorithms: Artificial Bee Colony (ABC), Hippopotamus Optimization (HO), and Sea-horse Optimizer (SHO). To facilitate the optimization process, the Storm Water Management Model (SWMM) provides the baseline simulations, while an Artificial Neural Network (ANN) is developed as a surrogate link to the optimization algorithms. The optimization problem is formulated as the minimization of both investment and operational costs of implementing LIDs. Results indicated that the ANN effectively predicted runoff values, while the optimization process identified minimum costs of $205,455, $206,749, and $205,251 for the ABC, HO, and SHO algorithms, respectively. Notably, the ABC algorithm demonstrated the shortest runtime among the evaluated algorithms. Furthermore, the findings revealed that the optimized LID configurations achieved a significant reduction in peak discharge, attenuating the peak flow from a baseline of 11.56 m<sup>3</sup>/s down to 3.75 m<sup>3</sup>/s.</p>

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Cost-Effective Urban Runoff Management Using Hybrid Simulation-Optimization of Low-Impact Development (LID)

  • 期刊论文

  • 发表日期：

    2026年4月24日

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  • Ramtin Moeini,
  • Seyed Hossein Hosseini,
  • Amir Mohamad Mandegar

摘要

Uncontrolled urban runoff can pose significant challenges to infrastructure and private property. While the implementation of Low-Impact Development (LID) strategies has begun to address these issues, designing LID configurations that effectively reduce runoff while simultaneously minimizing costs is a critical task that requires careful consideration. This research identifies the optimal configuration of Low-Impact Development (LID) practices for Sepahan Shahr, Isfahan, Iran, through a hybrid simulation-optimization framework. To mitigate urban runoff across three sub-catchments, bioretention, permeable pavement, and vegetated swales are evaluated. A core novelty of this study lies in the comparative performance analysis of three distinct metaheuristic algorithms: Artificial Bee Colony (ABC), Hippopotamus Optimization (HO), and Sea-horse Optimizer (SHO). To facilitate the optimization process, the Storm Water Management Model (SWMM) provides the baseline simulations, while an Artificial Neural Network (ANN) is developed as a surrogate link to the optimization algorithms. The optimization problem is formulated as the minimization of both investment and operational costs of implementing LIDs. Results indicated that the ANN effectively predicted runoff values, while the optimization process identified minimum costs of $205,455, $206,749, and $205,251 for the ABC, HO, and SHO algorithms, respectively. Notably, the ABC algorithm demonstrated the shortest runtime among the evaluated algorithms. Furthermore, the findings revealed that the optimized LID configurations achieved a significant reduction in peak discharge, attenuating the peak flow from a baseline of 11.56 m3/s down to 3.75 m3/s.