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A comparative machine learning approach to identify landslide triggering factors in northern Chilean Patagonia

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Abstract

Worldwide landslides correspond to one of the most dangerous geological events due to their destructive power and unpredictable nature. In the Chilean Patagonia, the SERNAGEOMIN (Chilean Geological Survey Service) has detected 2533 landslide events in the Northern Patagonia (42. 7∘S, 72. 4∘W) alone, a small area compared to the whole Chilean Patagonia. However, only 11 evens have known date. Consequently, it is not possible to associate temporal triggers and mechanisms that control such events, resulting in a lack of understanding of the factors that enable landslides. This work aims to detect landslides and identify the main environmental variables (climatic and geomorphological) that explain their occurrence using machine learning methods. We will address the following research questions: 1) How can a temporal landslide dataset be built using Landsat images and Google Earth Engine in Northern Patagonia? 2) Once the landslides and their timing have been detected, what are the main variables that condition the landslide processes? In our work, we developed a temporal dataset of landslides for the northern Patagonia of Chile. We used three machine learning approaches, where it was possible to identify the main environmental variables that allow us to predict their generation. Statistical models show that during the last 19 years, there has been complex interaction between different environmental variables that have influenced the activity of landslides. Climatic indices, indicators of extreme events, have a high incidence in the events’ predictive capacity. However, the most important are those linked to Patagonia’s tectonic context. In particular, the time elapsed after the eruptive event of the Chaitén volcano. Finally, the Liquiñe-Ofqui Fault System’s presence extending throughout the entire north of Patagonia has generated discontinuities at a general level, causing significant geomorphological instability. The study area has a relief in evolution and reactive to climatic conditions. Therefore, we highlight the need to understand better the interaction between geological and climatic processes and the future impact of these natural hazards. We emphasize the importance of analyzing landslide controls, considering both geomorphological variables and sporadic geological events.

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Notes

  1. Climatic data retrieved from: www.explorador.cr2.cl)

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Acknowledgements

We would like to thank the research unit at the University of La Frontera for their support with the English review. This study was possible thanks to the Chilean National Agency of Research and Development (ANID in Spanish) through the Program of International Cooperation (PCI in Spanish; grant no. PII-180008) and the “Fondecyt Iniciación program (grant no. 11170609).

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Authors and Affiliations

  1. Butamallin Research Center for Global Change, Universidad de La Frontera, Av. Francisco Salazar 01145, 4780000, Temuco, Chile

    Bastian Morales, Elizabet Lizama & Marcelo A. Somos-Valenzuela

  2. Department of Forest Sciences, Faculty of Agriculture and Forest Sciences, Universidad de La Frontera, Av. Francisco Salazar 01145, 4780000, Temuco, Chile

    Marcelo A. Somos-Valenzuela

  3. Faculty of Agricultural Engineering, Universidad de Concepción, Av. Vicente Mendez, 595, Chillan, Chile

    Mario Lillo-Saavedra

  4. Key Laboratory of Mountain Hazards and Earth Surface Processes, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, 610041, China

    Ningsheng Chen

  5. Department of Civil Engineering, Universidad de La Frontera, Av. Francisco Salazar 01145, 4780000, Temuco, Chile

    Ivo Fustos

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  1. Bastian Morales
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Correspondence to Marcelo A. Somos-Valenzuela.

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Morales, B., Lizama, E., Somos-Valenzuela, M.A. et al. A comparative machine learning approach to identify landslide triggering factors in northern Chilean Patagonia. Landslides 18, 2767–2784 (2021). https://doi.org/10.1007/s10346-021-01675-9

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