Landslide Susceptibility Mapping Using Machine Learning: A Danish Case Study

Mapping of landslides, conducted in 2021 by the Geological Survey of Denmark and Greenland (GEUS), revealed 3202 landslides in Denmark, indicating that they might pose a bigger problem than previously acknowledged. Moreover, the changing climate is assumed to have an impact on landslide occurrences...

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Published in:ISPRS International Journal of Geo-Information
Main Authors: Angelina Ageenko, Lærke Christina Hansen, Kevin Lundholm Lyng, Lars Bodum, Jamal Jokar Arsanjani
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2022
Subjects:
geo
Online Access:https://doi.org/10.3390/ijgi11060324
https://doaj.org/article/804b140af7194cf28b46e988ccf5d830
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spelling fttriple:oai:gotriple.eu:oai:doaj.org/article:804b140af7194cf28b46e988ccf5d830 2023-05-15T16:30:20+02:00 Landslide Susceptibility Mapping Using Machine Learning: A Danish Case Study Angelina Ageenko Lærke Christina Hansen Kevin Lundholm Lyng Lars Bodum Jamal Jokar Arsanjani 2022-05-01 https://doi.org/10.3390/ijgi11060324 https://doaj.org/article/804b140af7194cf28b46e988ccf5d830 en eng MDPI AG doi:10.3390/ijgi11060324 2220-9964 https://doaj.org/article/804b140af7194cf28b46e988ccf5d830 undefined ISPRS International Journal of Geo-Information, Vol 11, Iss 324, p 324 (2022) predictive modelling spatial prediction Denmark landslides logistic regression support vector machine geo manag Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2022 fttriple https://doi.org/10.3390/ijgi11060324 2023-01-22T17:17:34Z Mapping of landslides, conducted in 2021 by the Geological Survey of Denmark and Greenland (GEUS), revealed 3202 landslides in Denmark, indicating that they might pose a bigger problem than previously acknowledged. Moreover, the changing climate is assumed to have an impact on landslide occurrences in the future. The aim of this study is to conduct the first landslide susceptibility mapping (LSM) in Denmark, reducing the geographical bias existing in LSM studies, and to identify areas prone to landslides in the future following representative concentration pathway RCP8.5, based on a set of explanatory variables in an area of interest located around Vejle Fjord, Jutland, Denmark. A subset from the landslide inventory provided by GEUS is used as ground truth data. Three well-established machine learning (ML) algorithms—Random Forest, Support Vector Machine, and Logistic Regression—were trained to classify the data samples as landslide or non-landslide, treating the ML task as a binary classification and expressing the results in the form of a probability in order to produce susceptibility maps. The classification results were validated through the test data and through an external data set for an area located outside of the region of interest. While the high predictive performance varied slightly among the three models on the test data, the LR and SVM demonstrated inferior accuracy outside of the study area. The results show that the RF model has robustness and potential for applicability in landslide susceptibility mapping in low-lying landscapes of Denmark in the present. The conducted mapping can become a step forward towards planning for mitigative and protective measures in landslide-prone areas in Denmark, providing policy-makers with necessary decision support. However, the map of the future climate change scenario shows the reduction of the susceptible areas, raising the question of the choice of the climate models and variables in the analysis. Article in Journal/Newspaper Greenland Unknown Greenland Vejle Fjord ENVELOPE(-21.700,-21.700,70.750,70.750) ISPRS International Journal of Geo-Information 11 6 324
institution Open Polar
collection Unknown
op_collection_id fttriple
language English
topic predictive modelling
spatial prediction
Denmark
landslides
logistic regression
support vector machine
geo
manag
spellingShingle predictive modelling
spatial prediction
Denmark
landslides
logistic regression
support vector machine
geo
manag
Angelina Ageenko
Lærke Christina Hansen
Kevin Lundholm Lyng
Lars Bodum
Jamal Jokar Arsanjani
Landslide Susceptibility Mapping Using Machine Learning: A Danish Case Study
topic_facet predictive modelling
spatial prediction
Denmark
landslides
logistic regression
support vector machine
geo
manag
description Mapping of landslides, conducted in 2021 by the Geological Survey of Denmark and Greenland (GEUS), revealed 3202 landslides in Denmark, indicating that they might pose a bigger problem than previously acknowledged. Moreover, the changing climate is assumed to have an impact on landslide occurrences in the future. The aim of this study is to conduct the first landslide susceptibility mapping (LSM) in Denmark, reducing the geographical bias existing in LSM studies, and to identify areas prone to landslides in the future following representative concentration pathway RCP8.5, based on a set of explanatory variables in an area of interest located around Vejle Fjord, Jutland, Denmark. A subset from the landslide inventory provided by GEUS is used as ground truth data. Three well-established machine learning (ML) algorithms—Random Forest, Support Vector Machine, and Logistic Regression—were trained to classify the data samples as landslide or non-landslide, treating the ML task as a binary classification and expressing the results in the form of a probability in order to produce susceptibility maps. The classification results were validated through the test data and through an external data set for an area located outside of the region of interest. While the high predictive performance varied slightly among the three models on the test data, the LR and SVM demonstrated inferior accuracy outside of the study area. The results show that the RF model has robustness and potential for applicability in landslide susceptibility mapping in low-lying landscapes of Denmark in the present. The conducted mapping can become a step forward towards planning for mitigative and protective measures in landslide-prone areas in Denmark, providing policy-makers with necessary decision support. However, the map of the future climate change scenario shows the reduction of the susceptible areas, raising the question of the choice of the climate models and variables in the analysis.
format Article in Journal/Newspaper
author Angelina Ageenko
Lærke Christina Hansen
Kevin Lundholm Lyng
Lars Bodum
Jamal Jokar Arsanjani
author_facet Angelina Ageenko
Lærke Christina Hansen
Kevin Lundholm Lyng
Lars Bodum
Jamal Jokar Arsanjani
author_sort Angelina Ageenko
title Landslide Susceptibility Mapping Using Machine Learning: A Danish Case Study
title_short Landslide Susceptibility Mapping Using Machine Learning: A Danish Case Study
title_full Landslide Susceptibility Mapping Using Machine Learning: A Danish Case Study
title_fullStr Landslide Susceptibility Mapping Using Machine Learning: A Danish Case Study
title_full_unstemmed Landslide Susceptibility Mapping Using Machine Learning: A Danish Case Study
title_sort landslide susceptibility mapping using machine learning: a danish case study
publisher MDPI AG
publishDate 2022
url https://doi.org/10.3390/ijgi11060324
https://doaj.org/article/804b140af7194cf28b46e988ccf5d830
long_lat ENVELOPE(-21.700,-21.700,70.750,70.750)
geographic Greenland
Vejle Fjord
geographic_facet Greenland
Vejle Fjord
genre Greenland
genre_facet Greenland
op_source ISPRS International Journal of Geo-Information, Vol 11, Iss 324, p 324 (2022)
op_relation doi:10.3390/ijgi11060324
2220-9964
https://doaj.org/article/804b140af7194cf28b46e988ccf5d830
op_rights undefined
op_doi https://doi.org/10.3390/ijgi11060324
container_title ISPRS International Journal of Geo-Information
container_volume 11
container_issue 6
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