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

被引:13
|
作者
Lizama, Elizabet [1 ]
Morales, Bastian [1 ]
Somos-Valenzuela, Marcelo [1 ,2 ]
Chen, Ningsheng [3 ]
Liu, Mei [3 ]
机构
[1] Univ La Frontera, Butamallin Res Ctr Global Change, Av Francisco Salazar 01145, Temuco 4780000, Chile
[2] Univ La Frontera, Fac Agr & Forest Sci, Dept Forest Sci, Av Francisco Salazar 01145, Temuco 4780000, Chile
[3] Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Hazards & Earth Surface Proc, Chengdu 610041, Peoples R China
关键词
landslides; natural hazards; northern Patagonia; Villa Santa Lucia; landslide susceptibility; machine learning; EARTHQUAKE; CLIMATE; CLASSIFICATION; TECTONICS; INSIGHTS; MODEL; ANDES; AREA;
D O I
10.3390/rs14040907
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 x 10(6) m(3) almost destroyed Villa Santa Lucia 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.
引用
收藏
页数:20
相关论文
共 50 条
  • [1] A comparative machine learning approach to identify landslide triggering factors in northern Chilean Patagonia
    Bastian Morales
    Elizabet Lizama
    Marcelo A. Somos-Valenzuela
    Mario Lillo-Saavedra
    Ningsheng Chen
    Ivo Fustos
    Landslides, 2021, 18 : 2767 - 2784
  • [2] A comparative machine learning approach to identify landslide triggering factors in northern Chilean Patagonia
    Morales, Bastian
    Lizama, Elizabet
    Somos-Valenzuela, Marcelo A.
    Lillo-Saavedra, Mario
    Chen, Ningsheng
    Fustos, Ivo
    LANDSLIDES, 2021, 18 (08) : 2767 - 2784
  • [3] Landslide susceptibility mapping using ensemble machine learning methods: a case study in Lombardy, Northern Italy
    Xu, Qiongjie
    Yordanov, Vasil
    Amici, Lorenzo
    Brovelli, Maria Antonia
    INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2024, 17 (01)
  • [4] Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco, Italy
    Mehrabi, Mohammad
    NATURAL HAZARDS, 2022, 111 (01) : 901 - 937
  • [5] Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco, Italy
    Mohammad Mehrabi
    Natural Hazards, 2022, 111 : 901 - 937
  • [6] Landslide Susceptibility Mapping Using Machine Learning: A Danish Case Study
    Ageenko, Angelina
    Hansen, Laerke Christina
    Lyng, Kevin Lundholm
    Bodum, Lars
    Arsanjani, Jamal Jokar
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2022, 11 (06)
  • [7] A Comparison Study of Landslide Susceptibility Spatial Modeling Using Machine Learning
    Nurwatik, Nurwatik
    Ummah, Muhammad Hidayatul
    Cahyono, Agung Budi
    Darminto, Mohammad Rohmaneo
    Hong, Jung-Hong
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2022, 11 (12)
  • [8] Landslide Susceptibility Mapping Using Machine Learning: A Case Study of Oregon
    Wu, Bin
    Shi, Zhenming
    Peng, Ming
    GEOSHANGHAI 2024 INTERNATIONAL CONFERENCE, VOL 5, 2024, 1334
  • [9] Landslide Susceptibility Prediction Using Machine Learning Methods: A Case Study of Landslides in the Yinghu Lake Basin in Shaanxi
    Ma, Sheng
    Chen, Jian
    Wu, Saier
    Li, Yurou
    SUSTAINABILITY, 2023, 15 (22)
  • [10] LANDSLIDE SUSCEPTIBILITY MAPPING USING MACHINE LEARNING ALGORITHMS STUDY CASE AL HOCEIMA REGION, NORTHERN MOROCCO
    Himmy, Oussama
    Rhinane, Hassan
    GEOINFORMATION WEEK 2022, VOL. 48-4, 2023, : 153 - 158