The assessment of the vulnerability of communities prone to landslide related disasters is a topic that is growing in importance. Few studies discuss this issue and limited research has been carried out on the relationship between types of landslide and their potential impact on buildings and infrastructure. We outline a framework to undertake an assessment of the vulnerability of buildings to landslide utilising a similar framework used for assessing the vulnerability of buildings to tsunami damage. The framework is based on the development of an "elements at risk database" that takes into consideration the characteristics and use of the buildings, their importance for the local economy and the characteristics of the inhabitants (population density, age and so forth). The attributes that affect vulnerability are imported and examined within a GIS database which is used to visualise the physical, human and economic vulnerability. The results may have important implications for disaster management and emergency planning, and the database can be used by various end-users and stakeholders such as insurance companies, local authorities and the emergency services. The approach presented here can be integrated in to a wider more detailed "Framework for Landslide Risk and Vulnerability Assessment for Communities". We illustrate the potential of this framework and present preliminary results from Lichtenstein, Baden Württemberg, Germany.

To prepare for upcoming extreme events, decision makers, scientists and other stakeholders require a thorough understanding of the vulnerability of the built environment to natural hazards. A vulnerability index based on building characteristics (indicators) rather than empirical data may be an alternative approach to a comprehensive physical vulnerability assessment of the building stock. The present paper focuses on the making of such an index for dynamic flooding in mountain areas demonstrating the transferability of vulnerability assessment approaches between hazard types, reducing the amount of required data and offering a tool that can be used in areas were empirical data are not available. We use data from systematically documented torrential events in the European Alps to select and weight the important indicators using an all-relevant feature selection algorithm based on random forests. The permutation-based feature selection reduced the initial number of indicators from 22 to seven, decreasing in this way the amount of required data for assessing physical vulnerability and ensuring that only relevant indicators are considered. The new Physical Vulnerability Index (PVI) may be used in the mountain areas of Europe and beyond where only few empirical data are available supporting decision-making in reducing risk to dynamic flooding.