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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Solving multi-objective water management problems using evolutionary computation.

A Lewis1, M Randall2

  • 1Institute for Integrated and Intelligent Systems, Griffith University, Queensland, Australia.

Journal of Environmental Management
|September 8, 2017
PubMed
Summary
This summary is machine-generated.

This study presents a model for optimal crop selection using evolutionary computation and Pareto optimization to balance agricultural revenue and environmental flow deficits, crucial for water resource management amid climate change.

Keywords:
Evolutionary computationMulti-objective optimisationNon-dominated sorting genetic algorithm-IIPareto optimisationWater resource management

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Area of Science:

  • Agricultural Science
  • Environmental Science
  • Computational Intelligence

Background:

  • Water is a critical resource for global food security, especially with changing climate patterns.
  • Effective water resource management and decision-making tools are increasingly important.
  • Optimizing agricultural practices is key to sustainable food production.

Purpose of the Study:

  • To develop and present a model-based system for water resource management.
  • To incorporate evolutionary computation and Pareto optimization for decision support.
  • To model optimal crop selection strategies under varying climatic conditions.

Main Methods:

  • Utilizing evolutionary computation techniques.
  • Applying Pareto optimization for multi-objective decision-making.
  • Developing a model-based system for water management simulations.

Main Results:

  • The system rapidly generates optimal crop selection solutions.
  • Trade-off solutions balancing net revenue and environmental flow deficits are identified.
  • The model effectively handles dry, average, and wet year scenarios.

Conclusions:

  • The developed system provides valuable insights for water management and agricultural planning.
  • It supports participative decision-making by offering detailed optimal solutions.
  • The approach is adaptable to various factors like climate and economic conditions.