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Mapping wind erosion hazard with regression-based machine learning algorithms.

Hamid Gholami1, Aliakbar Mohammadifar2, Dieu Tien Bui3,4

  • 1Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran. hgholami@hormozgan.ac.ir.

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This summary is machine-generated.

Advanced machine learning models effectively mapped wind erosion susceptibility in Isfahan, Iran. Digital Elevation Model (DEM), precipitation, and vegetation (NDVI) were key factors influencing erosion risk.

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Area of Science:

  • Environmental Science
  • Geoscience
  • Data Science

Background:

  • Wind erosion poses a significant environmental hazard, impacting land productivity and ecosystems.
  • Accurate mapping of land susceptibility to wind erosion is crucial for effective land management and mitigation strategies.
  • Machine learning offers powerful tools for analyzing complex environmental data and predicting spatial patterns.

Purpose of the Study:

  • To map land susceptibility to wind erosion hazard in Isfahan province, Iran.
  • To evaluate the performance of 16 advanced regression-based machine learning methods for wind erosion susceptibility mapping.
  • To identify the most critical factors controlling wind erosion in the study area.

Main Methods:

  • Tested 16 regression-based machine learning algorithms, including Monotone Multi-layer Perception Neural Network (MMLPNN), Spline Generalized Additive Model (SGAM), Cforest, Boosting Generalized Additive Model (BGAM), and Stochastic Gradient Boosting (SGB).
  • Quantified multicollinearity among 13 controlling factors using Tolerance Coefficient (TC) and Variance Inflation Factor (VIF).
  • Assessed model performance using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), and Taylor diagrams.

Main Results:

  • Five models (MMLPNN, SGAM, Cforest, BGAM, SGB) demonstrated high prediction accuracy for land susceptibility to wind erosion.
  • Digital Elevation Model (DEM), precipitation, and Normalized Difference Vegetation Index (NDVI) were identified as the most critical factors controlling wind erosion.
  • Regression-based machine learning models proved efficient for mapping wind erosion susceptibility.

Conclusions:

  • Advanced machine learning techniques, particularly MMLPNN, SGAM, Cforest, BGAM, and SGB, are highly effective for mapping land susceptibility to wind erosion.
  • Understanding the influence of topographic, climatic, and vegetation factors is essential for predicting wind erosion risk.
  • The study provides a robust framework for utilizing machine learning in environmental hazard assessment and land management.