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An objective absence data sampling method for landslide susceptibility mapping.

Yasin Wahid Rabby1, Yingkui Li2, Haileab Hilafu3

  • 1Department of Engineering, Wake Forest University, Winston-Salem, NC, USA. yasinwr@wfu.edu.

Scientific Reports
|January 31, 2023
PubMed
Summary
This summary is machine-generated.

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This study introduces an objective Mahalanobis distance (MD) method for selecting non-landslide data, improving landslide susceptibility maps. The MD-based approach enhances accuracy and consistency compared to subjective slope-based sampling.

Area of Science:

  • Geosciences
  • Remote Sensing
  • Geographic Information Systems (GIS)

Background:

  • Landslide susceptibility mapping accuracy relies heavily on landslide location data and absence data sampling strategies.
  • Existing absence data sampling methods, like slope-based approaches, can be subjective and impact map quality.
  • Objective methods are needed to improve the reliability of landslide susceptibility assessments.

Purpose of the Study:

  • To propose and validate an objective method for determining critical values for sampling absence data using Mahalanobis distances (MD).
  • To compare the effectiveness of MD-based absence data sampling with traditional slope-based methods in landslide susceptibility mapping.
  • To enhance the accuracy and consistency of landslide susceptibility maps.

Main Methods:

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  • Determined a critical Mahalanobis distance (MD) value (23.69) using Chi-square distribution with 14 degrees of freedom.
  • Utilized 15 landslide causal factors (e.g., slope, aspect, plan curvature) for analysis.
  • Generated landslide susceptibility maps using the random forest model and assessed accuracy with ROC curves and Kappa index, and consistency with Seed Cell Area Index (SCAI).

Main Results:

  • The proposed MD-based sampling method yielded a landslide susceptibility map with high model fitting (0.87), prediction (0.85), and Kappa (0.77) values.
  • Maps generated using MD-based sampling demonstrated superior consistency (SCAI) compared to those from slope-based sampling.
  • While slope-based sampling also showed high accuracy, its consistency was lower, and the method is deemed highly subjective.

Conclusions:

  • Mahalanobis distance (MD)-based absence data sampling provides an objective and effective approach for improving landslide susceptibility mapping.
  • The MD method enhances both the accuracy and consistency of landslide susceptibility maps.
  • This study recommends adopting MD-based sampling over subjective slope-based methods for more reliable landslide risk assessments.