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Dynamic Evaluation of Intensive Land Use Based on Objective Empowerment by Entropy Method and Neural Network
Ting Yang1, Hanlie Cheng2,3, Hailian Zhao4
1School of Public Administration, Chongqing Technology and Business University, Chongqing 400076, China.
Computational Intelligence and Neuroscience
|September 1, 2022
Summary
This study introduces a new model for evaluating land intensive use, improving upon traditional methods. The findings reveal that land use intensity is higher in western Chongqing compared to eastern areas.
Area of Science:
- Environmental Science
- Urban Planning
- Geographic Information Systems
Background:
- Traditional land intensive use evaluation models exhibit limitations, including linear correlations and inability to adapt to dynamic land use environments.
- Existing methods fail to account for indicator direction differences across intervals and dynamic changes in land use types.
Purpose of the Study:
- To develop a more objective and adaptable evaluation model for land intensive use in development zones.
- To improve the realistic fit of land use evaluation by considering nonlinear correlations and dynamic changes.
Main Methods:
- Standardization of quadratic function indicators for nonlinear correlation.
- Entropy assignment method for objective weight determination.
- Application of the improved model to assess land intensive use in different regions of Chongqing.
Main Results:
- The study identified significant regional disparities in land intensive use within Chongqing.
- Western and central Chongqing demonstrated a higher overall level of land intensive use.
- Northeastern and southeastern Chongqing exhibited lower levels of land intensive use, indicating an east-west gradient.
Conclusions:
- The proposed nonlinear, dynamic evaluation model offers a more realistic assessment of land intensive use.
- The findings provide valuable insights for regional land use planning and policy-making in Chongqing.
- Addressing the identified disparities can promote more sustainable and intensive land utilization across the region.
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