Shallow and Deep-Seated Landslide Differentiation Using Support Vector Machines: A Case Study of the Chuetsu Area, Japan

  • Author(s): Jie Dou, Uttam Paudel, Takashi Oguchi, Shoichiro Uchiyama, and Yuichi S. Hayakawa
  • DOI: 10.3319/TAO.2014.12.02.07(EOSI)
  • Keywords: Airborne Lidar DEM, Shallow and deep-seated landslide, Differentiation, SVMs

Landslides are one of the most destructive geological disasters affecting Japan every year, resulting in huge losses in life and property. Numerous susceptibility studies have been conducted to minimize the risk of landslides; however, most of these studies do not differentiate landslide types. This study examines the differences in landslide depth, volume and the risk imposed between shallow and deep-seated landslide types. Shallow and deep-seated landslide prediction is useful in utilizing emergency resources by prioritizing target areas while responding to sediment related disasters. This study utilizes a 2-m DEM derived from airborne Light detection and ranging (Lidar), geological information and support vector machines (SVMs) to study the 1225 landslides triggered by the M 6.8 Chuetsu earthquake in Japan and the successive aftershocks. Ten factors, including elevation, slope, aspect, curvature, lithology, distance from the nearest geologic boundary, density of geo- logic boundaries, distance from drainage network, the compound topographic index (CTI) and the stream power index (SPI) derived from the DEM and a geological map were analyzed. Iterated over 10 random instances the average training and testing accuracy of landslide type prediction was found to be 89.2 and 77.8%, respectively. We also found that the overall accuracy of SVMs does not rapidly decrease with a decrease in training samples. The trained model was then used to prepare a map showing probable future landslides differentiated into shallow and deep-seated landslides.

Abstract

Landslides are one of the most destructive geological disasters affecting Japan every year, resulting in huge losses in life and property. Numerous susceptibility studies have been conducted to minimize the risk of landslides; however, most of these studies do not differentiate landslide types. This study examines the differences in landslide depth, volume and the risk imposed between shallow and deep-seated landslide types. Shallow and deep-seated landslide prediction is useful in utilizing emergency resources by prioritizing target areas while responding to sediment related disasters. This study utilizes a 2-m DEM derived from airborne Light detection and ranging (Lidar), geological information and support vector machines (SVMs) to study the 1225 landslides triggered by the M 6.8 Chuetsu earthquake in Japan and the successive aftershocks. Ten factors, including elevation, slope, aspect, curvature, lithology, distance from the nearest geologic boundary, density of geo- logic boundaries, distance from drainage network, the compound topographic index (CTI) and the stream power index (SPI) derived from the DEM and a geological map were analyzed. Iterated over 10 random instances the average training and testing accuracy of landslide type prediction was found to be 89.2 and 77.8%, respectively. We also found that the overall accuracy of SVMs does not rapidly decrease with a decrease in training samples. The trained model was then used to prepare a map showing probable future landslides differentiated into shallow and deep-seated landslides.

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