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<p>This paper presents a comprehensive framework for redesigning urban drainage systems (UDSs) under deep uncertainties associated with simulation model parameters. The flood inundation depth is estimated using the integrated one-dimensional and two-dimensional simulations of urban runoff using the personal computer storm water management model (PCSWMM). Critical uncertain parameters are first identified, and their ranges of variation are established. Subsequently, a flood damage function is developed based on these parameters and the simulated flood volume. A one-dimensional rainfall-runoff simulation model, SWMM, is integrated with a non-dominated sorting genetic algorithm II (NSGA-II) with two objectives: minimizing implementation and maintenance costs and minimizing flood damage costs. To derive robust solutions, a new constraint is introduced into the NSGA-II framework. This constraint restricts the variation in objective functions due to uncertainties in input parameters, thereby enhancing solution stability. Each solution on the robust front comprises the locations and coverage areas of different types of low impact development (LID) practices. Finally, social choice methods are utilized to identify optimal urban runoff management strategies from robust options by incorporating stakeholder preferences. The approach ensures social alignment while enabling robustness evaluation through comparative model analysis. The effectiveness of the proposed methodology is demonstrated through its application to the East Tehran UDS, which serves a population of over three million people. By identifying the surface roughness coefficients of green spaces and streets as the most critical uncertain parameters, a robust front, comprising 119 strategies, is derived. Based on the robust front, implementing the proposed framework reduces flood volume by up to 36.1% and flood damage by up to 63.4% compared to scenarios without LID practices. Moreover, the selected strategy achieves a 35.3% reduction in implementation and maintenance costs, along with a 62.3% decrease in flood damage costs, while also enhancing stakeholders’ satisfaction.</p>

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Redesigning urban drainage systems under uncertainty: a robust multi-objective approach for data-sparse catchments

  • 期刊论文

  • 发表日期：

    2025年7月8日

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  • Helia Ghaffari,
  • Sara Haghbin,
  • Najmeh Mahjouri

摘要

This paper presents a comprehensive framework for redesigning urban drainage systems (UDSs) under deep uncertainties associated with simulation model parameters. The flood inundation depth is estimated using the integrated one-dimensional and two-dimensional simulations of urban runoff using the personal computer storm water management model (PCSWMM). Critical uncertain parameters are first identified, and their ranges of variation are established. Subsequently, a flood damage function is developed based on these parameters and the simulated flood volume. A one-dimensional rainfall-runoff simulation model, SWMM, is integrated with a non-dominated sorting genetic algorithm II (NSGA-II) with two objectives: minimizing implementation and maintenance costs and minimizing flood damage costs. To derive robust solutions, a new constraint is introduced into the NSGA-II framework. This constraint restricts the variation in objective functions due to uncertainties in input parameters, thereby enhancing solution stability. Each solution on the robust front comprises the locations and coverage areas of different types of low impact development (LID) practices. Finally, social choice methods are utilized to identify optimal urban runoff management strategies from robust options by incorporating stakeholder preferences. The approach ensures social alignment while enabling robustness evaluation through comparative model analysis. The effectiveness of the proposed methodology is demonstrated through its application to the East Tehran UDS, which serves a population of over three million people. By identifying the surface roughness coefficients of green spaces and streets as the most critical uncertain parameters, a robust front, comprising 119 strategies, is derived. Based on the robust front, implementing the proposed framework reduces flood volume by up to 36.1% and flood damage by up to 63.4% compared to scenarios without LID practices. Moreover, the selected strategy achieves a 35.3% reduction in implementation and maintenance costs, along with a 62.3% decrease in flood damage costs, while also enhancing stakeholders’ satisfaction.