Landslide Susceptibility and Hazard derived from a Landslide Inventory using Data Mining – an Australian case studyFaculty of Engineering - Papers (Archive)
AbstractThe University of Wollongong landslide research team has developed a comprehensive GIS-based Landslide Inventory of the 550 km2 Wollongong Local Government Area (WLGA) and surrounding regions, just south of Sydney in the State of New South Wales, Australia. This inventory includes 575 landslide sites and forms the crucial centerpiece of the methodology reported in this paper. The inventory identifies 2.95% of a 188 km2 escarpment study area to be covered by landsliding reported during the last 120 years. With GIS-based data sets, a ‘slide’ category landslide susceptibility map layer has been developed with the aid of ‘knowledge-based’ data-mining techniques. Susceptibility zones have been classified as (a) known landslides, (b) high susceptibility with ~ 8% of the area subject to landslides (contains 57% of the known landslides), (c) moderate susceptibility with 4% of the area subject to landslides (contains 35% of known landslides), (d) low susceptibility with 0.85% of the area subject to landslides (contains 3.7% of known landslides), and (e) very low susceptibility with <0.1% of the area subject to landsliding (represents 71% of the study area). It is important to note that the high susceptibility zone identifies over 2,300 hectares of land, outside of known landslides, as being highly susceptible to landsliding. The ‘slide’ category susceptibility maps have been upgraded to hazard level maps with identification and labelling of site specific frequency, volume and ‘profile’ angles for each landslide. The average landslide frequency of occurrence for each susceptibility zone has been determined.
Citation InformationP. Flentje, David Stirling and R. N. Chowdhury. "Landslide Susceptibility and Hazard derived from a Landslide Inventory using Data Mining – an Australian case study" (2007)
Available at: http://works.bepress.com/dstirling/9/