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July 30, 2026International Journal of Digital EarthOpen Access

Coupling multi-level agent system with ant colony optimization for land use allocation in Wuhan, China

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Authors

BPBowen PangYLYaolin LiuYLYaolin Liu

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Overview

Randomized trial evaluates land use optimization in Wuhan, suggesting enhanced spatial simulation methods.

Key Points

  • This study aims to improve land use allocation by integrating machine learning with a multi-level agent system.
  • Constructed a machine learning-based spatiotemporal data system for agent perception
  • Developed MLAS-ACO, a model coupling multi-level agent system with ant colony optimization
  • Conducted analysis using a case study in Wuhan, China.
  • CatBoost-based suitability evaluation achieved F1 score > 0.88
  • MLAS-ACO improved overall objective by 6.84% and convergence speed by 18.7%
  • Scenario analysis suggested optimal land allocation in suitable zones.

Cite This Study

Pang et al. (2026) studied this question.

synapsesocial.com/papers/6a6af56d60e2b924d3ea1a1fhttps://doi.org/10.1080/17538947.2026.2705634
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