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Updated: May 17, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Multi-objective optimization of urban stormwater systems.

Bozhen Wang1, Yongwei Gong1, Xin Li2

  • 1Key Laboratory of Urban Stormwater System and Water Environment, Ministry of Education, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China.

Journal of Environmental Management
|May 15, 2025
PubMed
Summary
This summary is machine-generated.

Optimizing urban stormwater systems using a multi-objective model significantly reduces flood risk and costs. Key adjustments in storage, inlet timing, and discharge capacity enhance system performance and resilience against extreme rainfall events.

Keywords:
Extreme design rainfallInfoworks ICM modelMulti-objective optimizationNon-dominated sorting genetic algorithm (NSGA-II)Urban stormwater systems

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Area of Science:

  • Environmental Engineering
  • Urban Hydrology
  • Climate Change Adaptation

Background:

  • Urban flooding is exacerbated by urbanization and climate change, overwhelming traditional stormwater management.
  • Existing systems struggle with capacity, connectivity, and cost-effectiveness in flood risk reduction.
  • There is a critical need for optimized stormwater solutions balancing multiple objectives.

Purpose of the Study:

  • To develop and apply a multi-objective optimization model for urban stormwater systems.
  • To simultaneously improve flood risk mitigation, facility efficiency, and cost-effectiveness.
  • To provide decision support for enhancing urban resilience to excessive runoff.

Main Methods:

  • Utilized the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization.
  • Developed a comprehensive evaluation index system for flood risk, facility utilization, and economic costs.
  • Applied the model to a case study under a 30-year return period rainfall scenario.

Main Results:

  • Optimized stormwater storage increased to 27% of upstream flood volume.
  • Inlet timing was optimized to 44 mm/h, and discharge capacity enhanced to 0.8 m³/s.
  • Significant improvements in system performance, flood risk reduction, and cost-effectiveness were demonstrated.

Conclusions:

  • Scientific management of key decision variables is crucial for optimizing stormwater systems.
  • The developed optimization approach offers valuable decision support for urban flood management.
  • Enhanced stormwater systems are vital for cities facing increasing flood risks due to climate change and urbanization.