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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A genetic-algorithm-aided stochastic optimization model for regional air quality management under uncertainty.

Xiaosheng Qin1, Guohe Huang, Lei Liu

  • 1Division of Environmental and Water Resource Engineering, School of Civil and Environmental Engineering, Nanyang Technological University, Singapore. xiaoshengqin@gmail.com

Journal of the Air & Waste Management Association (1995)
|January 28, 2010
PubMed
Summary

This study introduces a genetic-algorithm-aided stochastic optimization (GASO) model for regional air quality management. The model effectively incorporates uncertainty into cost-effective pollution control strategies, providing risk and cost information for decision-making.

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Area of Science:

  • Environmental Science
  • Operations Research
  • Computational Science

Background:

  • Regional air quality management faces significant uncertainty in model parameters.
  • Developing cost-effective pollution control strategies requires explicit consideration of these uncertainties.
  • Existing optimization frameworks may not adequately address stochastic elements in air quality modeling.

Purpose of the Study:

  • To develop a novel optimization model for regional air quality management under uncertainty.
  • To integrate genetic algorithm (GA) and Monte Carlo simulation within a chance-constrained programming (CCP) framework.
  • To generate least-cost air pollution control strategies that account for parameter uncertainties.

Main Methods:

  • Developed a genetic-algorithm-aided stochastic optimization (GASO) model.
  • Incorporated GA for solution seeking and Monte Carlo simulation for feasibility assessment.
  • Utilized a stochastic chance-constrained programming (CCP) framework to handle parameter uncertainties.

Main Results:

  • The GASO model successfully integrated uncertainty into the optimization process for air quality management.
  • The model generated a range of potential air pollutant treatment options, detailing associated risks and costs.
  • Case study demonstrated the model's applicability in regional air pollution control scenarios.

Conclusions:

  • The proposed GASO model provides a robust approach for managing regional air quality under uncertainty.
  • Decision-making can be informed by analyzing trade-offs between treatment costs and system failure risks.
  • The method offers valuable insights for developing effective and resilient pollution control strategies.