EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Regional landslide susceptibility mapping using tree-based machine learning techniques

Hamish Mitchell, James Brennan, Claire Burke, Kamil Kluza, Laura Ramsamy, and Markela Zeneli
Hamish Mitchell et al.
  • Climate X, Science & Technology, United Kingdom of Great Britain – England, Scotland, Wales (

The identification of assets susceptible to landslide-related damage is critical for planners, managers, and decision-makers in developing effective mitigation strategies. Recent applications of machine learning and data mining methods have demonstrated their use in geotechnical assessments including the spatial evaluation of landslide susceptibility.

At Climate X, we utilise tree-based machine learning techniques alongside geographic information system and remote sensing data to map landslide susceptibility across Great Britain. We compile several conditioning factors—including topographic, subsurface, land use, and climate-related data—and combine them with over 18,000 landslide instances, recorded in National Landslide Database. We evaluate the capabilities of several techniques including, decision tree, bagged tree, random forest, and balanced random forest (applies random undersampling of the majority, non-landslide class) for landslide susceptibility modelling. Several performance evaluation indices (area under receiver operator characteristic curve (AUC), precision, recall, F1 score) were used to assess and compare the performance of models. We show that the random forest is the most accurate of our models with an AUC of ​94.7%. Our results demonstrate that tree-based algorithms form a robust method to analyse regional landslide susceptibility and provide new insights into locations susceptible to landslide-related damage across Great Britain.

How to cite: Mitchell, H., Brennan, J., Burke, C., Kluza, K., Ramsamy, L., and Zeneli, M.: Regional landslide susceptibility mapping using tree-based machine learning techniques, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8286,, 2022.