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This paper outlines a framework for risk assessment of landslides triggered by earthquakes and rainfall in urban buildings in the city of Medellín - Colombia, applying a model that uses a geographic information system (GIS). We applied a computer model that includes topographic, geological, geotechnical and hydrological features of the study area to assess landslide hazards using the Newmark's pseudo-static method, together with a probabilistic approach based on the first order and second moment method (FOSM). The physical vulnerability assessment of buildings was conducted using structural fragility indexes, as well as the definition of damage level of buildings via decision trees and using Medellin's cadastral inventory data. The probability of occurrence of a landslide was calculated assuming that an earthquake produces horizontal ground acceleration ( A h ) and considering the uncertainty of the geotechnical parameters and the soil saturation conditions of the ground. The probability of occurrence was multiplied by the structural fragility index values and by the replacement value of structures. The model implemented aims to quantify the risk caused by this kind of disaster in an area of the city of Medellín based on different values of A h and an analysis of the damage costs of this disaster to buildings under different scenarios and structural conditions. Currently, 62% of “Valle de Aburra” where the study area is located is under very low condition of landslide hazard and 38% is under low condition. If all buildings in the study area fulfilled the requirements of the Colombian building code, the costs of a landslide would be reduced 63% compared with the current condition. An earthquake with a return period of 475 years was used in this analysis according to the seismic microzonation study in 2002. [ABSTRACT FROM AUTHOR]

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]

Abstract: Recently, the cities of Yerba Buena and Tucuman in Tucumán Province, Argentina, have been affected by several storm flood events, causing hundreds of people to be evacuated from their homes. Heavy intensity rainfall, new housing developments covering previously permeable grounds, and old drainage systems are the main causes for this situation. This paper presents a simple approach of urban flood hazard assessment in a region where primary data are scarce. The objectives of this study are to develop a GIS-aided urban flood hazard zoning of the two cities applying multicriteria decision analysis and to evaluate it by means of uncertainty and sensitivity analysis. The research methodology focused on the analysis of those variables that control the water routing when high peak flows exceed the drainage-system capacity. The model incorporates five parameters: distance to the drainage channels, topography (heights and slopes), ground water table depths, and urban land use. A final hazard map for each category is obtained using an algorithm that combines factors in weighted linear combinations. The urban flood hazard map shows that the southeast part of San Miguel de Tucumán has the highest flood hazard over an extended area as a consequence of the conjunction of lowlands with slopes under 0.6% and the presence of urban stream channels with poor maintenance plan. In this area, several neighbourhoods have been flooded. The model was evaluated by the error propagation method and global sensitivity analysis to assess the uncertainty and the relative importance of model input factors. Finally, the influence of criterion weights are analyzed ranging from 25% to 75% of their original values showing a robust behaviour. [Copyright &y& Elsevier]