Improved landslide susceptibility mapping using unsupervised and supervised collaborative machine learning models

ABSTRACT

Datasets containing recorded landslide and non-landslide samples can greatly influence the performance of machine learning (ML) models, which are commonly used in landslide susceptibility mapping (LSM). However, the non-landslide samples cannot be directly obtained. In this study, a pattern-based approach is proposed to improve the LSM process, constructing unsupervised machine learning (UML) – supervised machine learning (SML) collaborative models in which the non-landslide samples can be reasonably selected. Two UML models, the Gaussian mixture model (GMM) and K-means, are introduced to sample the non-landslide datasets with four sampling selections (abbreviated as A, B, C and D, respectively). Then non-landslide patterns recognised by the UML models are learned by the random forest (RF). A new sensitivity index, accuracy improvement ratio (AIR), is defined to evaluate the superiority of these sampling selections. Compared with the GMM-RF model, the K-means-RF model is more capable of recognising non-landslide patterns and providing sufficient and reliable non-landslide samples. The sampling selection A of the K-means-RF with an AIR value of 2.3 is regarded as the best selection. The results indicate that the UML-SML model based on the pattern-based approach offers an effective strategy to find the non-landslide samples and has a better solution to the LSM.

KEYWORDS:

• Landslide susceptibility
• machine learning
• remote sensing
• earthquake
• risk

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Basic Science Center Program for Multiphase Evolution in Hypergravity of the National Natural Science Foundation of China [grant number 51988101]; the Young Scientists Fund of the National Natural Science Foundation of China [grant number 41907243]; the National Key R& D Program of China [grant number 2019YFC1806001].