Earthquake is one of the natural disasters which threaten many lives every year. It is impossible to prevent earthquakes from occurring; however, it is possible to predict the building damage, human and property losses in advance to mitigate the adverse effects of the catastrophe. Seismic vulnerability assessment is a complex uncertain spatial decision making problem due to intrinsic uncertainties such as lack of complete data, vagueness in experts’ comments and uncertainties in the numerical data/relations. It is important to identify and model the incorporated uncertainties of seismic vulnerability assessment in order to obtain realistic predictions. Fuzzy sets theory can model the vagueness in weights of the selected criteria and relationships of the criteria with building damage. Dempster’s combination rule is useful for fusion of information on the vulnerability of the buildings which leads to decreased uncertainty of the results. However, when there is a conflict among information sources, classical Dempster rule of combination is not efficient. This paper analyses the uncertainty sources in a geospatial information system (GIS)-based seismic vulnerability assessment of buildings and then focuses on assessing the efficiency of Dempster rule of combination in the fusion of the information sources for the seismic vulnerability assessment. Tabriz, a historical and earthquake prone city in the north west of Iran was selected as the study area. The results verified that some inconsistencies among information sources exist which are important to be considered while proposing a method for the fusion of the information in order to obtain vulnerability assessments with less uncertainty. Based on the assessed building damage, the number of probable victims was estimated. The produced physical and social seismic vulnerability maps provide the required information for urban planners and administrators to reduce property and human losses through pre-earthquake mitigation and preparedness plans efficiently.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited