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Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility

Dae-Hong Min1, Hyung-Koo Yoon2

  • 1Department of Construction and Disaster Prevention Engineering, Daejeon University, Daejeon, 300-716, Korea.

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|March 24, 2021
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Summary

Machine learning (ML) offers a new deterministic approach for landslide risk assessment (LRA), overcoming data limitations. This method effectively uses four key variables for reliable LRA, simplifying complex geotechnical analyses.

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Area of Science:

  • Geotechnical Engineering
  • Machine Learning Applications
  • Environmental Science

Background:

  • Deterministic models are common for landslide risk assessment (LRA) but struggle with data acquisition.
  • Machine learning (ML) presents a potential solution to enhance LRA accuracy and efficiency.

Purpose of the Study:

  • To propose a novel deterministic method for LRA using ML algorithms.
  • To identify the most critical variables for accurate landslide risk prediction.

Main Methods:

  • Utilized linear regression and neural networks with backpropagation (gradient descent, Levenberg-Marquardt (LM), Bayesian regularization (BR)).
  • Constructed an 1800-item dataset using measured and geostatistically generated data.
  • Employed Random Forest (RF) to determine variable importance.

Main Results:

  • LM and BR backpropagation methods showed high coefficients of determination.
  • Identified shear strength, soil thickness, elastic modulus, and fine content as highly reliable variables for LRA.
  • Demonstrated that ML can perform LRA effectively with only four key variables.

Conclusions:

  • ML provides a viable and efficient alternative for deterministic landslide risk assessment.
  • Reducing the number of input variables to four key factors enhances LRA reliability, especially when data is scarce.