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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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The optimization of Low Impact Development placement considering life cycle cost using Genetic Algorithm.

Jeanne Jinhui Huang1, Meng Xiao1, Yu Li2

  • 1College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China.

Journal of Environmental Management
|February 18, 2022
PubMed
Summary
This summary is machine-generated.

Genetic Algorithm (GA) optimizes Low Impact Development (LID) selection and layout for urban stormwater management. This approach effectively reduces runoff and aids decision-making in planning LID strategies.

Keywords:
Genetic algorithmLife cycle costLow impact developmentStorm water management model

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Area of Science:

  • Environmental Engineering
  • Urban Hydrology
  • Sustainable Water Management

Background:

  • Urban runoff and non-point source pollution pose significant challenges to stormwater management.
  • Determining optimal Low Impact Development (LID) facilities and layouts is crucial but complex due to various constraints.
  • Existing methods face uncertainties in integrating factors like budget, land use, and climate.

Purpose of the Study:

  • To develop an optimized method for selecting and arranging LID facilities within a sub-catchment.
  • To address the uncertainties and challenges in LID planning by employing an optimization algorithm.
  • To enhance the scientific basis for decision-making in urban stormwater management.

Main Methods:

  • Utilized Genetic Algorithm (GA) for optimizing the selection and layout of LID.
  • Simulated urban runoff using the Environmental Protection Agency (EPA) Storm Water Management Model (SWMM).
  • Encoded LID planning as binary variables (0 and 1) within the GA framework.
  • Defined multiple optimization objectives: runoff reduction, LID area, and life cycle cost.

Main Results:

  • A combination of LID approaches proved most effective for long-term runoff reduction over 10 years of rainfall events.
  • Evaluated the influence of land area and cost priorities on LID selection.
  • Bio-Retention facilities were favored when prioritizing LID area, while Green Roofs were recommended when prioritizing cost.

Conclusions:

  • Genetic Algorithm (GA) is a feasible and effective tool for optimizing LID planning in urban areas.
  • The proposed method, integrating SWMM and GA, provides a scientific basis for LID decision-making.
  • This approach can assist stakeholders in developing more effective stormwater management systems.