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Updated: Sep 24, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A simulation-optimization approach for supporting conservative water allocation under uncertainties.

Yanpeng Cai1, Tong Li1, Yi Zhang2

  • 1Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou, 510006, China; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China.

Journal of Environmental Management
|May 7, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid model for sustainable water resource allocation in the Yalong River Basin. The integrated approach optimizes water distribution, minimizing shortages and maximizing benefits for improved management.

Keywords:
Fuzzy credibilityInterval two-stageNeural networkOptimal allocation of water resourcesUnbiased greyYalong river basin

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Operations Research

Background:

  • Water resource allocation is complex, especially in river basins facing increasing demand and uncertainty.
  • Accurate water demand prediction and optimized allocation are crucial for sustainable development.
  • Existing methods often struggle to account for the inherent uncertainties in water resource systems.

Purpose of the Study:

  • To propose a hybrid integrated model for water resource allocation in the Yalong River Basin.
  • To develop a robust framework that accounts for uncertainties in water demand and allocation.
  • To provide decision support for optimizing water resource management and policy-making.

Main Methods:

  • Integration of unbiased grey model (UGM) and artificial neural network (ANN) for water demand prediction.
  • Utilizing grey correlation analysis and eXtreme Gradient Boosting (XGboost) to link socio-economic indicators with water demand.
  • Development of an interval two-stage fuzzy credibility-constrained programming (ITFCP) model to handle uncertainties.

Main Results:

  • The developed hybrid ITFCP model successfully predicted water demands and allocated resources for planning years 2020, 2025, and 2030.
  • Analysis revealed system benefits at different credibility levels and identified water shortages for users across sub-regions.
  • The model provided actionable suggestions for optimizing water resource allocation.

Conclusions:

  • The hybrid UGM-ANN-ITFCP model offers a superior approach to water resource allocation compared to previous methods.
  • This integrated framework enhances decision-making for sustainable and profitable water management policies.
  • The study demonstrates the effectiveness of incorporating uncertainty analysis in water resource allocation models.