Intergration of GIS Using GEOSTAtistical INterpolation Techniques (Kriging) (GEOSTAINT-K) in Deterministic Models for Landslide Susceptibility Analysis (LSA) at Kota Kinabalu, Sabah, Malaysia

摘要

A practical application for landslide susceptibility analysis (LSA) based on GEOSTAtistical INterpolation Techniques (Kriging) (GEOSTAINT-K) for a deterministic model was used to calculate the factor of safety (FOS) and failure probabilities for the area of Kota Kinabalu, Sabah. In this paper, the LSA value can be expressed by a FOS value, which is the ratio of forces that make the slope fail and those that prevent the slope from failing. A geotechnical engineering properties data base has been developed on the basis of a series of parameter maps such as effective cohesion (C’), unit weight of soil (g), depth of failure surface (Z), height of ground water table (Zw), Zw/Z dimensionless (m), unit weight of water (gw), slope surface inclination (?) and effective angle of shearing resistance (f). Taking into consideration the cause of the landslide, identified as groundwater change, two scenarios of landslide activity were studied. Scenario 1 considered the minimum groundwater level recorded corresponding to the actual situation of the most recent landslide while Scenario 2 considered the reverse. A simple method (infinite slope model) for error propagation was used to calculate the variance of the FOS and the probability that will be less than 1 for each pixel. The highest probability value of the various scenarios was selected for each pixel and final LSA 1 (scenario 1) and LSA 2 (scenario 2) maps were constructed. The validation between the examined LSA 1 and LSA 2 maps and the results of the landslide distribution map (LDM) were evaluated. This deterministic model had higher prediction accuracy. The prediction accuracy was 81 % and 85 %, respectively. In general for both factors, the LSA 2 map showed higher accuracy compared to the LSA 1 map. The resulting LSA map can be used by local administrators or developers to locate areas prone to landslides, determine the land use suitability and organize more detailed analysis of the “hot spot” areas identified.