## Integration Of GIS In Deterministic Model (Infinite Slope) For Landslide Susceptibility Analysis (LSA) At Kota Kinabalu Area, Sabah, Malaysia

**Writers :** Rodeano Roslee, Tajul Anuar Jamaluddin & Mustapa Abd. Talip

**Date :** 23-24 June 2012

**Publisher :** National Geoscience Conference, 2012 (NGC 2012)

**Location :** Pullman Hotel, Kuching

**Abstract :**

A practical application for landslide susceptibility analysis (LSA) based on two dimensional deterministic slope stability (infinite slope model) (DSS-ISM) 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 (g), depth of failure surface (Z), height of ground water table (Zw), Zw/Z dimensionless (m), unit weight of water (gw), slope surface inclination (β) and effective angle of shearing resistance (f). Taking into consideration the cause of the landslide, identified as groundwater change, two scenarios of landslide activity have been studied. Scenario 1 considers the minimum groundwater level recorded corresponding to the actual situation of the most recent landslide while the scenario 2 is vice-versa. A simple method 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. 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 1 and LSA 2 maps and result of landslide distribution map (LDM) were evaluated. This DSS-ISM had higher prediction accuracy. The prediction accuracy is 81 % (LSA 1) and 84 % (LSA 2), respectively. Overall the case of both factors, LSA 2 model used showed a higher accuracy than cases of LSA 1 model used. 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.