Regret-Based Decision Making for Total Maximum Daily Load Allocation under Climate Change Scenarios; Application of Charged System Search Algorithm

نوع مقاله : مقاله پژوهشی

نویسندگان

1 Former Graduate Student, School of Environmental Engineering,Iran University of Science and Technology, Tehran, Iran

2 Prof., School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

چکیده

Although temporal and spatial severity of climate change remains uncertain, its occurrence and impacts on water resources is quite perceivable. Under any uncertain condition, such as climate change, proper and sustainable pollutant load allocation to receiving water bodies remains as a serious challenge. In the absence of statistical data and reliable probability distribution function for uncertain parameters, planners may use non-probabilistic approaches for tackling the imposed uncertainties. Among the common non-probabilistic approaches, the regret method is a robust and successfully used method for decision analysis. This paper presents an integrated approach for pollutant load allocation under uncertain climate condition. It integrates an efficient optimization algorithm and a physical quality simulation model in a regret-based decision analysis platform. The proposed system establishes a linkage between loads and receiving water conditions to maximize the dischargeable total maximum daily load (TMDL). Water quality responses of the receiving water body under different loads are estimated using QUAL2K simulation model. Maximization of total daily load under varying scenarios is carried out with the charged system search (CSS) algorithm. Effects on uncertainties in occurrence and severity of the assumed scenarios are analyzed in a non-probabilistic framework with minimizing the maximum and total regret (MMR, MTR), and the best scenario is proposed for implementation. Performance of the proposed approach is tested using the data from New River at the Salton outlet.

کلیدواژه‌ها


عنوان مقاله [English]

Regret-Based Decision Making for Total Maximum Daily Load Allocation under Climate Change Scenarios; Application of Charged System Search Algorithm

نویسندگان [English]

  • Elham Faraji 1
  • Abbas Afshar 2
1 Former Graduate Student, School of Environmental Engineering,Iran University of Science and Technology, Tehran, Iran
2 Prof., School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
چکیده [English]

Although temporal and spatial severity of climate change remains uncertain, its occurrence and impacts on water resources is quite perceivable. Under any uncertain condition, such as climate change, proper and sustainable pollutant load allocation to receiving water bodies remains as a serious challenge. In the absence of statistical data and reliable probability distribution function for uncertain parameters, planners may use non-probabilistic approaches for tackling the imposed uncertainties. Among the common non-probabilistic approaches, the regret method is a robust and successfully used method for decision analysis. This paper presents an integrated approach for pollutant load allocation under uncertain climate condition. It integrates an efficient optimization algorithm and a physical quality simulation model in a regret-based decision analysis platform. The proposed system establishes a linkage between loads and receiving water conditions to maximize the dischargeable total maximum daily load (TMDL). Water quality responses of the receiving water body under different loads are estimated using QUAL2K simulation model. Maximization of total daily load under varying scenarios is carried out with the charged system search (CSS) algorithm. Effects on uncertainties in occurrence and severity of the assumed scenarios are analyzed in a non-probabilistic framework with minimizing the maximum and total regret (MMR, MTR), and the best scenario is proposed for implementation. Performance of the proposed approach is tested using the data from New River at the Salton outlet.

کلیدواژه‌ها [English]

  • Pollutant Load Allocation
  • climate change
  • Regret Analysis
  • Charged system search algorithm
  • Uncertainty
  • robust
  • Total Maximum Daily Load
  • TMDL
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