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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Interval multistage joint-probabilistic integer programming approach for water resources allocation and management.

J J Gu1, G H Huang, P Guo

  • 1College of Architecture and Art, Heifei University of Technology, Heifei 230009, China.

Journal of Environmental Management
|July 13, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces an interval multistage joint-probabilistic integer programming method for water resource regulation, optimizing reservoir operations and addressing water scarcity and excess, crucial for sustainable water management.

Keywords:
DynamicJoint probabilityMultistageScenariosUncertaintyWater resources

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Watershed Planning within a Quantitative Scenario Analysis Framework
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

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Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Area of Science:

  • Environmental Science
  • Operations Research
  • Water Resource Management

Background:

  • Water resource regulation faces challenges with uncertain data, including interval, probabilistic, and joint probabilistic information.
  • Multi-stage planning requires methods that capture dynamic variability and inter-stage linkages.

Purpose of the Study:

  • To develop an interval multistage joint-probabilistic integer programming method for water resource regulation.
  • To effectively handle diverse forms of uncertain data in water management planning.
  • To optimize the joint operation of multiple reservoirs and downstream water distribution.

Main Methods:

  • Developed an interval multistage joint-probabilistic integer programming model.
  • Incorporated interval data, probability distributions, and joint probabilities.
  • Performed sensitivity analysis on constraint violations and system benefits.

Main Results:

  • The method successfully integrates various data uncertainties in water resource planning.
  • Sensitivity analysis revealed a strong correlation between constraint violations and system benefits.
  • The model effectively addressed downstream water shortages and excess water distribution issues.

Conclusions:

  • The developed method provides a robust framework for complex water resource regulation problems.
  • It enables optimized long-term water usage for reservoirs and downstream areas.
  • The approach is validated through a case study on reservoir joint operation in the Huaihe River basin.