Granular computing and Dempster–Shafer integration in seismic vulnerability assessment

Abstract

Iran is one of the seismically active areas of the world due to its position in the Alpine-Himalayan mountain system. Tehran has several faults hence huge earthquakes will permeates human settlement there. Production of seismic vulnerability map could help disaster management organizations to develop and implement a plan to promote awareness of earthquake vulnerability and implementation of seismic vulnerability reduction measures in Tehran. The process of seismic vulnerability assessment is a supervised classification problem which undertaken by implementation of classification rules obtained from relationships between classes defined by a set of attributes and a unified decision of a group of experts. Therefore, seismic vulnerability assessment is a multidisciplinary problem which needs a multi criteria decision making. The influencing factors make the problem and the process of decision making a complicated disaster management problem. To overcome this problem, this paper proposes an integrated model based upon the granular computing and Dempster–Shafer to extract classification rules for classification of urban areas regarding seismic vulnerability. One of the significant properties of granular computing is induction of more compatible rules having no inconsistency. In this paper, Dempster–Shafer theory is used to integrate and model the conflict among different experts’ viewpoints to get an informed decision regarding the measure of seismic vulnerability in each statistical unit in the study area.