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Landslide Susceptibility Mapping At Kota Kinabalu, Sabah Using Factor Analysis Model (FAM)

Writers : Rodeano Roslee, Tajul Anuar Jamaluddin, Mustapa Abd. Talip, James Anthony Collin & Budirman Rudding

Date : 11-12 June 2011

Publisher : National Geoscience Conference 2011

Location : The Puteri Pacific Johor Bahru & Persada International Convention Centre, Johor

Abstract :

The aim of the study was to prepare a landslide susceptibility map (LSM) of Kota Kinabalu area, Sabah. The rapid development had a spill over effect in the Kota Kinabalu area where lands was cleared for the construction of highways, high-rise buildings, industrial, housing area and several other heavy infrastructures. These activities had, besides spurring economic growth, indirectly also caused landslide risk management problems. Hence, for further development of the area, it is vital to assess the probability of landslide occurrence. For this purpose, the statistical approach (factor analysis model - FAM) based on GIS (Geographical Information System), has been applied to assess the landslide susceptibility of the area. Landslide susceptibility is defned as quantitative or qualitative assessment of the classifcation, volume (or area) and spatial distribution of landslides which exist or potentially may occur in an area. Susceptibility may also include a description of the velocity and intensity of the existing or potential landsliding. The FAM is a data reduction technique used to reduce a large number of variables to a smaller set of underlying factors that summarize the essential information contained in the variables. More frequently, this model consists of a statistical comparison between landslide distribution as the dependant variables and a number of separate instability factors (input parameters). This approach makes it possible to calculate the “rating” of an individual input parameter. The method is based on the assumption that landslides will always occur in the same geological, geomorphological, hydrogeological and climatic conditions as in the past and the procedure considers a number of environmental factors that are thought to be connected with landslide occurrence. The following input parameters were compared and analysed: geology, geodynamic features, slope conditions, hydrology/hydrogeology, types of landuse, and engineering characteristics of soils and rocks. The data layers, in which each factor was subdivided into a convenient number of classes, were separately overlain and statistically compared with the landslide distribution map (LDM). Subsequently, the landslide density was calculated and the weighted value was determined for each individual class. The fnal landslide susceptibility value was expressed as the sum of all parameter classes ranked according to the calculated landslide density for each class. In terms of landslide susceptibility, the  resulted of Kota Kinabalu area suggests that 10% of the area can be categorised as Very Low Susceptibility, 16% as Low Susceptibility, 14% as Moderate Susceptibility, 48% as High Susceptibility and 12% as Very High Susceptibility.  In contrast, ‘high’ and ‘very high’ susceptibilities areas represent the steep slope segments with either buildings or roads and in terms of land use planning is vice-versa. The landslide susceptibility map was analysed with known landslide locations and verifed. This FAM had higher prediction accuracy (93.59 % reliability). The resulting LSAM 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 identifed “hot spot” areas. This study also shows the ability of geospatial technology as powerful integrated tools.