Landslide Susceptibility Analysis (LSA) using Deterministic Model (Infinite Slope) (DESSISM) in the Kota Kinabalu Area, Sabah, Malaysia

Deterministic slope stability (DSS) infinite slope model (ISM) landslide susceptibility analysis (LSA) failure probability A practical application for landslide susceptibility analysis (LSA) based on two dimensional deterministic slope stability (infinite slope model) (DESSISM) was used to calculate factor of safety (FOS) and failure probabilities for the area of Kota Kinabalu, Sabah. LSA is defined as quantitative or qualitative assessment of the classification, volume (or area) and spatial distribution of landslides which exist or potentially may occur in an area. In this paper, LSA value can be expressed by a FOS, which is the ratio between the forces that make the slope fail and those that prevent the slope from failing. An 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 (), depth of failure surface (Z), height of ground water table (Zw), Zw/Z dimensionless (m), unit weight of water (w), slope surface inclination (β) and effective angle of shearing resistance (). Taking into consideration the cause of the landslide, identified as groundwater change, the maximum groundwater level recorded corresponding to the actual situation of the most recent landslide is considered in this study. The highest probability value of the various scenarios was selected for each pixel and final LSA map were constructed. It has been found from this study that β and Zw parameters have the higher influence on landslide instability. The result validation between the examined LSA map and result of landslide distribution map (LDM) were evaluated. This DESSISM had higher prediction accuracy. The prediction accuracy is 84%. The resulting LSA maps can be used by local administration or developers to locate areas prone to landslide area, determine the land use suitability area and to organize more detailed analysis in the identified “hot spot” areas.