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GIS-based non-grain cultivated land susceptibility prediction using data mining methods.

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  • 1Shangluo Branch, Shaanxi Provincial Land Engineering Construction Group, Xi'an, 710075, China. ztyymy2021@163.com.

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This study developed a non-grain cultivated land (NCL) susceptibility map using an optimized Extreme Gradient Boosting (XGBoost) model. The Particle Swarm Optimization (PSO) algorithm enhanced prediction accuracy, highlighting key environmental factors.

Keywords:
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Area of Science:

  • Environmental Science
  • Geographic Information Systems (GIS)
  • Machine Learning Applications

Background:

  • Non-grain cultivated land (NCL) prediction is crucial for land use planning.
  • Existing models require optimization for improved accuracy.
  • Understanding susceptibility factors is key to effective land management.

Purpose of the Study:

  • To create a non-grain cultivated land (NCL) susceptibility map.
  • To optimize the Extreme Gradient Boosting (XGBoost) model using Particle Swarm Optimization (PSO).
  • To evaluate the performance of the optimized XGBoost model.

Main Methods:

  • Utilized 184 historical non-grain cultivated land (NCL) areas.
  • Incorporated 16 NCL susceptibility conditioning factors (NCLSCFs).
  • Applied Particle Swarm Optimization (PSO) to optimize the Extreme Gradient Boosting (XGBoost) model.

Main Results:

  • The PSO-optimized XGBoost model demonstrated high predictive performance (AUC=0.96).
  • Key susceptibility factors identified include slope, rainfall, fault density, distance from fault, and drainage density.
  • The model significantly outperformed other machine learning algorithms.

Conclusions:

  • Particle Swarm Optimization (PSO) effectively enhances machine learning model performance for NCL prediction.
  • The developed susceptibility map provides valuable insights for land use planning and management.
  • Meta-heuristic algorithms offer powerful tools for environmental modeling.