|
| | Abstract | | Evaluation of landslide risk with high accuracy is an important issue to maintain the stability of slopes efficiently. However, it is difficult to evaluate the landslide risk with high accuracy because the causes of landslides are generally diverse and complex. In this study, we conducted the comprehensive evaluation of the landslide risk by using the machine learning technique, called SVM (Ohishi et al. 2007), based on the slope investigation data which were acquired in Nara Prefecture in 2007. As a result, we were able to evaluate the landslide risk with higher accuracy than the conventional method. In particular, it becomes clear that it is possible to evaluate the landslide risk with high accuracy in a large area, adding the geological classification data to the slope investigation data, whose evaluation was carried out in the standard uniform for all slopes. |
|
|
|
| |
|