Discrete rough set analysis of two different soil-behavior-induced landslides in National Shei-Pa Park, Taiwan

AbstractThe governing factors that influence landslide occurrences are complicated by the different soil conditions at various sites. To resolve the problem, this study focused on spatial information technology to collect data and information on geology. GIS, remote sensing and digital elevation model (DEM) were used in combination to extract the attribute values of the surface material in the vast study area of Shei-Pa National Park, Taiwan. The factors influencing landslides were collected and quantification values computed. The major soil component of loam and gravel in the Shei-Pa area resulted in different landslide problems. The major factors were successfully extracted from the influencing factors. Finally, the discrete rough set (DRS) classifier was used as a tool to find the threshold of each attribute contributing to landslide occurrence, based upon the knowledge database. This rule-based knowledge database provides an effective and urgent system to manage landslides. NDVI (Normalized Difference Vegetation Index), VI (Vegetation Index), elevation, and distance from the road are the four major influencing factors for landslide occurrence. The landslide hazard potential diagrams (landslide susceptibility maps) were drawn and a rational accuracy rate of landslide was calculated. This study thus offers a systematic solution to the investigation of landslide disasters