Comparative review of data-driven landslide susceptibility models: case study in the Eastern Andes mountain range of Colombia

Wilmar Calderón-Guevara (), Mauricio Sánchez-Silva, Bogdan Nitescu and Daniel F. Villarraga
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Wilmar Calderón-Guevara: Universidad de los Andes
Mauricio Sánchez-Silva: Universidad de los Andes
Bogdan Nitescu: Universidad de los Andes
Daniel F. Villarraga: Universidad de los Andes

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 113, issue 2, No 12, 1105-1132

Abstract: Abstract Estimating the likelihood of landslides has proven to be critical for development and protection of infrastructure (e.g. pipelines, roads) and urban settlements. Currently, for regional studies of landslide susceptibility only qualitative or statistical evaluations are possible due to the large spatial variability of geological properties, topography, rainfall patterns, etc. In this paper, we explore an alternative to these approaches using data-driven methodologies to determine landslide susceptibility. We give special attention to the use of geographical information systems, machine learning and statistical techniques to build landslide susceptibility maps. These methods have input as fourteen key causative factors that might influence landslides occurrence. Additionally, feature extraction and feature selection are performed to evaluate if dimensionality reduction increases the prediction accuracy of the machine learning models. The models were compared using a case study in the Eastern Cordillera of Colombia, where the best performing model achieved a predictive performance of $$93.07\%$$ 93.07 % .

Keywords: Landslides; Susceptibility; Machine learning; Data-analytics; GIS (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-022-05339-2

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