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Abstract: This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping. [Copyright &y& Elsevier]

Risk is defined as the probability or expectation of loss resulting in the form of death, injury, property damage, economic disruption, disease, relocation, and disruption of community activities caused by a disaster. Having awareness concerning risk levels in an area will expedite all planning on disaster management to be carried out more efficiently and effectively. This is because every area that has a different level of disaster risk should also be managed in different ways. Security and mitigation measures should be emphasized in areas with high levels of disaster risk compared to areas with low levels of disaster risk. Therefore, this study would like to identify the level of flood risk in Beaufort, Sabah. This is because Beaufort one of the areas that often experience floods every year. The level of risk is obtained based on the following model or equation, risk = hazard + exposure + vulnerability. To obtain the results of the study, the application of Geographic Information System or its acronym Geographic Information System (GIS) and fuzzy analysis were used. The results of the analysis found that 3.9% of the total area of Beaufort was categorized as areas with very high-risk levels while another 4.4% and 26.4% were categorized as areas with high and moderate risk levels. The remaining 65.3% are categorized as areas with low and very low-risk levels. Although the percentage of the area that has a very high level of flood risk is only 3.9%, it was found that the area has a town, two small towns, and seven villages. This study is expected to have significant implications in terms of the development of flood disaster management in the Beaufort district in particular and Malaysia in general. [ABSTRACT FROM AUTHOR]