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Updated: Jul 13, 2025

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Study on landslide susceptibility mapping with different factor screening methods and random forest models.

Tengfei Gu1,2, Jia Li1, Mingguo Wang3

  • 1Faculty of Geography, Yunnan Normal University, Kunming, Yunnan Province, China.

Plos One
|October 12, 2023
PubMed
Summary
This summary is machine-generated.

Factor screening significantly improves landslide susceptibility mapping (LSM) model accuracy. The information gain ratio (IGR) method combined with a random forest (RF) model yielded the best prediction performance, highlighting its utility for landslide risk assessment.

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Area of Science:

  • Geosciences
  • Environmental Science
  • Data Science

Background:

  • Landslide susceptibility mapping (LSM) is crucial for predicting landslide occurrences.
  • The selection of input factors significantly impacts the accuracy of predictive models.
  • Effective factor screening is a critical initial step in building robust LSM models.

Purpose of the Study:

  • To evaluate the impact of different factor screening methods on landslide susceptibility prediction accuracy.
  • To compare the performance of various factor screening techniques in the context of LSM.
  • To identify the most influential factors for landslide prediction in Jingdong County.

Main Methods:

  • Construction of a landslide database using 136 landslide events and 11 selected factors from Jingdong County.
  • Application of four factor screening methods: information gain ratio (IGR), GeoDetector, Pearson correlation coefficient, and multicollinearity test (MT).
  • Development of a random forest (RF) model for LSM, utilizing datasets processed by each screening method, followed by accuracy validation using confusion matrices and ROC curves.

Main Results:

  • Factor screening demonstrably enhances the accuracy of LSM models compared to using all original factors.
  • The IGR-RF model achieved the highest Area Under the Curve (AUC) value of 0.9334, outperforming the non-screened model (AUC=0.9194).
  • The IGR-RF model exhibited superior prediction performance, accurately classifying the largest proportion of landslides into the very high susceptibility zone (51.22%).

Conclusions:

  • Factor screening is a beneficial preprocessing step for improving LSM model performance.
  • The information gain ratio (IGR) method is highly effective for selecting relevant factors in landslide susceptibility modeling.
  • NDVI, elevation, and aspect were identified as the most significant factors influencing landslides in the study area.