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Mapping landslide susceptibility and types using Random Forest

Taalab, K; Cheng, T; Zhang, Y; (2018) Mapping landslide susceptibility and types using Random Forest. Big Earth Data , 2 (2) pp. 159-178. 10.1080/20964471.2018.1472392. Green open access

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Abstract

Landslides are one of the most destructive natural hazards; they can drastically alter landscape morphology, destroy man-made structures, and endanger people’s life. Landslide susceptibility maps (LSMs), which show the spatial likelihood of landslide occurrence, are crucial for environmental management, urban planning, and minimizing economic losses. To date, the majority of research into data mining LSM uses small-scale case studies focusing on a single type of landslide. This paper presents a data mining approach to producing LSM for a large, heterogeneous region that is susceptible to multiple types of landslides. Using a case study of Piedmont, Italy, a Random Forest algorithm is applied to produce both susceptibility maps and classification maps. These maps are combined to give a highly accurate (over 85% classification accuracy) LSM which contains a large amount of information and is easy to interpret. This novel method of mapping landslide susceptibility demonstrates the efficacy of Random Forest to produce highly accurate susceptibility maps for a large heterogeneous region without the need for multiple susceptibility assessments.

Type: Article
Title: Mapping landslide susceptibility and types using Random Forest
Open access status: An open access version is available from UCL Discovery
DOI: 10.1080/20964471.2018.1472392
Publisher version: https://doi.org/10.1080/20964471.2018.1472392
Language: English
Additional information: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: Landslide susceptibility; landslide type; random forest
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10076818
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