Understanding Landslide Susceptibility in Northern Chilean Patagonia: A Basin-Scale Study Using Machine Learning and Field Data

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 4; p. 907
• Main Authors: Lizama, Elizabet, Morales, Bastian, Somos-Valenzuela, Marcelo, Chen, Ningsheng, Liu, Mei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2022
• Subjects: Algorithms; Basins; Climate change; Critical infrastructure; Drainage density; Earthquakes; Fieldwork; Floods; Geological processes; Geology; Geomorphology; Infrastructure; landslide susceptibility; Landslides; Landslides & mudslides; Learning algorithms; Lithology; Machine learning; Mathematical models; Morphology; Mountains; natural hazards; northern Patagonia; Precipitation; Qualitative analysis; Remote sensing; Sedimentary rocks; Seismic activity; Soil moisture; Spatial data; Statistical models; Villa Santa Lucia; Volcanic activity; Zeolites; Chile
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14040907

The interaction of geological processes and climate changes has resulted in growing landslide activity that has impacted communities and ecosystems in northern Chilean Patagonia. On 17 December 2017, a catastrophic flood of 7 × 106 m3 almost destroyed Villa Santa Lucía and approximately 3 km of the southern highway (Route 7), the only land route in Chilean Patagonia that connects this vast region from north to south, exposing the vulnerability of the population and critical infrastructure to these natural hazards. The 2017 flood produced a paradigm shift on the analysis scale to understand the danger to which communities and their infrastructure are exposed. Thus, in this study, we sought to evaluate the susceptibility of landslides in the Yelcho and Rio Frio basins, whose intersection represents the origin of this great flood. For this, we used two approaches, (1) geospatial data in combination with machine learning methods using different training configurations and (2) a qualitative analysis of the landscape considering the geological and geomorphological conditions through fieldwork. For statistical modeling, we used an inventory of landslides that occurred between 2008 and 2017 and a total of 17 predictive variables, which are geoenvironmental, climatological and environmental triggers derived from volcanic and seismic activity. Our results indicate that soil moisture significantly impacted spatial susceptibility, followed by lithology, drainage density and seismic activity. Additionally, we observed that the inclusion of climatic predictors and environmental triggers increased the average performance score of the models by up to 3–5%. Based on our results, we believe that the wide distribution of volcanic–sedimentary rocks hydrothermally altered with zeolites in the western mountains of the Yelcho and Rio Frio basin are highly susceptible to generating large-scale landslides. Therefore, the town of Villa Santa Lucia and the “Carretera Austral” (Route 7) are susceptible to new landslides coming mainly from the western slope. This requires the timely implementation of measures to mitigate the impact on the population and critical infrastructure.