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A Novel Rule-based Approach In Mapping Landslide Susceptibility.

Majid Shadman Roodposhti1, Jagannath Aryal2, Biswajeet Pradhan3,4

  • 1Discipline of Geography and Spatial Sciences, School of Technology, Environments and Design, University of Tasmania, Churchill Ave, Hobart, TAS 7005, Australia. majid.shadman@utas.edu.au.

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Summary
This summary is machine-generated.

This study introduces a transparent, rule-based system for landslide susceptibility mapping (LSM). The novel approach enhances understanding of landslide causes and reduces subjectivity in classification, achieving high accuracy.

Keywords:
GISShannon entropyTasmanialandslide susceptibility mapping (LSM)uncertainty

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Area of Science:

  • Geosciences
  • Environmental Science
  • Geographic Information Systems

Background:

  • Existing landslide susceptibility mapping (LSM) methods lack transparency and exhibit subjectivity.
  • Understanding landslide-conditioning factors at the local scale is crucial but often hindered by opaque models.
  • High subjectivity in re-classifying susceptibility scores reduces the quality of LSM products.

Purpose of the Study:

  • To develop and validate a novel, transparent rule-based system for landslide susceptibility mapping (LSM).
  • To improve the understanding of landslide-conditioning factors and reduce subjectivity in susceptibility classification.
  • To assess the reliability and performance of the proposed LSM method at the pixel-level.

Main Methods:

  • A rule-based system was developed, relating landslide-conditioning factors without logical operators.
  • Shannon entropy was used to determine factor priorities and rule uncertainties.
  • Rule-level uncertainties were mapped for pixel-level reliability assessment, followed by If-Then rule application for classification.

Main Results:

  • The proposed rule-based LSM method demonstrated high performance with an Area Under the Curve (AUC) of 0.934 in a case study.
  • The approach provided transparency in identifying factor priorities and states/values for each pixel.
  • Uncertainties associated with susceptibility rules were accessible, interpretable, and replicable.

Conclusions:

  • The novel rule-based system offers a transparent and less subjective alternative for landslide susceptibility mapping.
  • The method enhances insights into LSM processes by clearly defining rules and uncertainties.
  • This approach is beneficial for deriving interpretable and reliable landslide susceptibility information.