Landslides are among the most serious of geohazards in the Xi＇an Region, Shaanxi, China, and are responsible for extensive human and property loss. In order to understand the distribution of landslides and assess the...Landslides are among the most serious of geohazards in the Xi＇an Region, Shaanxi, China, and are responsible for extensive human and property loss. In order to understand the distribution of landslides and assess their associated hazards in this region, we used a combination of frequency analysis, logistic analysis, and Geographic Information System （GIS） analysis, with consideration of the spatial distribution of landslides. Using the GIS approach, the five key factors of surface topography, including slope gradient, topographic wetness index （TWI）, height difference, profile curvature and slope aspect, were considered. First, the distribution and frequency of landslides were considered in relation to all of the five factors in each of three sub-regions susceptible to landslides （Qin Mountain, Li Mountain, and Loess Tableland）. Secondly, each factor＇s influence was deter- mined by a logistic regression method, and the relative importance of each of these independent variables was evaluated. Finally, a landslide susceptibility map was generated using GIS tools. Locations that had recorded landslides were used to validate the results of the landslide susceptibility map and the accuracy obtained was above 84%. The validation proved that there is sufficient agreement between the susceptibility map and existing records of landslide occurrences. The logistic regression model produced acceptable results （the areas under the Receiver Operating Characteristics （ROC） curve were 0.865, 0.841, and 0.924 in the Qin Mountain, Li Mountain and Loess Tableland）. We are confident that the results of this study can be useful in preliminary planning for land use, particularly for construction work in high-risk areas.展开更多
文摘Landslides are among the most serious of geohazards in the Xi＇an Region, Shaanxi, China, and are responsible for extensive human and property loss. In order to understand the distribution of landslides and assess their associated hazards in this region, we used a combination of frequency analysis, logistic analysis, and Geographic Information System （GIS） analysis, with consideration of the spatial distribution of landslides. Using the GIS approach, the five key factors of surface topography, including slope gradient, topographic wetness index （TWI）, height difference, profile curvature and slope aspect, were considered. First, the distribution and frequency of landslides were considered in relation to all of the five factors in each of three sub-regions susceptible to landslides （Qin Mountain, Li Mountain, and Loess Tableland）. Secondly, each factor＇s influence was deter- mined by a logistic regression method, and the relative importance of each of these independent variables was evaluated. Finally, a landslide susceptibility map was generated using GIS tools. Locations that had recorded landslides were used to validate the results of the landslide susceptibility map and the accuracy obtained was above 84%. The validation proved that there is sufficient agreement between the susceptibility map and existing records of landslide occurrences. The logistic regression model produced acceptable results （the areas under the Receiver Operating Characteristics （ROC） curve were 0.865, 0.841, and 0.924 in the Qin Mountain, Li Mountain and Loess Tableland）. We are confident that the results of this study can be useful in preliminary planning for land use, particularly for construction work in high-risk areas.
