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Spatial prediction of urban landslide susceptibility based on topographic factors using boosted trees

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Abstract

As global warming accelerates, abnormal weather events are occurring more frequently. In the twenty-first century in particular, hydrological disruption has increased as water flows have changed globally, causing the strength and frequency of hydrological disasters to increase. The damage caused by such disasters in urban areas can be extreme, and the creation of landslide susceptibility maps to predict and analyze the extent of future damage is an urgent necessity. Therefore, in this study, probabilistic and data mining approaches were utilized to identify landslide-susceptible areas using aerial photographs and geographic information systems. Areas where landslides have occurred were located through interpretation of aerial photographs and field survey data. In addition, topographic maps generated from aerial photographs were used to determine the values of topographic factors. A frequency ratio (FR) model was utilized to examine the influences of topographic, soil and vegetation factors on the occurrence of landslides. A total of 23 variables that affect landslide frequency were selected through FR analysis, and a spatial database was constructed. Finally, a boosted tree model was applied to determine the correlations between various factors and landslide occurrence. Correlations among related input variables were calculated as predictor importance values, and sensitivity analysis was performed to quantitatively analyze the impact of each variable. The boosted tree model showed validation accuracies of 77.68 and 78.70% for the classification and regression algorithms using receiver operating characteristic curve, respectively. Reliable accuracy can provide a scientific basis to urban municipalities for policy recommendations in the management of urban landslides.

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Acknowledgements

This research was conducted by Korea Environment Institute (KEI) with support of a Grant (NRF-2018R1D1A1B07041203) by the Program through the National Research Foundation of Korea (NRF). This research was supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Ministry of Science and ICT.

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Authors and Affiliations

  1. Department of Geoinformatics, University of Seoul, Seoul, South Korea

    Sunmin Lee

  2. Center for Environmental Assessment Monitoring, Environmental Assessment Group, Korea Environment Institute (KEI), Sejong, South Korea

    Sunmin Lee & Moung-Jin Lee

  3. Geological Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), Daejeon, South Korea

    Saro Lee

  4. University of Science and Technology, Daejeon, South Korea

    Saro Lee

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  1. Sunmin Lee
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  2. Moung-Jin Lee
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Correspondence to Moung-Jin Lee or Saro Lee.

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Lee, S., Lee, MJ. & Lee, S. Spatial prediction of urban landslide susceptibility based on topographic factors using boosted trees. Environ Earth Sci 77, 656 (2018). https://doi.org/10.1007/s12665-018-7778-7

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