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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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An integrated intelligent model for simulating and optimizing regional water resource sustainability under multiple

Shule Li1, Qiuming Pei2

  • 1School of Economics, Sichuan University of Science & Engineering, Yibin, 644000, China.

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|March 9, 2026
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Summary

This study introduces an Integrated Intelligent Model (IIM) for water resource management in water-scarce regions. The model optimizes economic growth and environmental goals through AI forecasting and multi-system co-optimization, promoting sustainability.

Keywords:
BP neural networkIntegrated intelligent modelSustainabilityThe optimal pathWater resource

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Artificial Intelligence

Background:

  • Rapid urbanization intensifies pressures on regional water resources, including demand, scarcity, and pollution.
  • Existing frameworks lack integration of long-term dynamic forecasting with coordinated optimization of complex coupled systems.

Purpose of the Study:

  • To propose and apply an Integrated Intelligent Model (IIM) for water resource management in water-scarce regions.
  • To develop a novel framework integrating AI-driven forecasting with dynamic multi-objective optimization.

Main Methods:

  • Developed an Integrated Intelligent Model (IIM) combining GM(1,1) and BP neural network forecasting with a multi-objective optimization system.
  • Coupled socio-economic, water resource, and environmental subsystems.
  • Simulated five progressive constraint scenarios (S0-S4) for sustainable pathways from 2018-2035.

Main Results:

  • Scenario S3, with 5% annual groundwater reduction and 3% wastewater reduction, optimized economic growth (6.73% GRP) and environmental goals.
  • Achieved a 59.4% reduction in water intensity, 36% decrease in Chemical Oxygen Demand (COD) emissions, and 16.0% groundwater share.
  • Identified structural economic transformation (tertiary industry to 64.5%) and wastewater reuse as key optimization mechanisms.

Conclusions:

  • An intelligent modeling framework combining AI forecasting and multi-system co-optimization provides adaptive decision support for water resource management.
  • Demonstrated a non-linear trade-off between economic and environmental goals, with diminishing marginal returns from overly stringent constraints.
  • The IIM promotes water resource sustainability in highly stressed regions.