Tehran's seismic vulnerability classification using granular computing approach

Tehran, capital of Iran, is located on a number of known and unknown faults which make this mega city exposed to huge earthquakes. Determining locations and intensity of seismic vulnerability of a city is considered as a complicated disaster management problem. As this problem generally depends on various criteria, one of the most important challenges concerned is the existence of uncertainty regarding inconsistency in combining those effective criteria. The emergence of uncertainty in seismic vulnerability map results to some biases in risk management which has multilateral effects in dealing with the consequences of the earthquake. To overcome this problem, this paper proposes a new approach for Tehran's seismic vulnerability classification based on granular computing. One of the most significant properties of this method is inference of accurate rules having zero entropy from predefined classification undertaken based on training datasets by the expert. Furthermore, not-redundant covering rules will be extracted for consistent classification where one object maybe classified with two or more nonredundant rules. In this paper, Tehran statistical zones (3,173 according to 1996 census) are considered as the study area. Since this city has not experienced a disastrous earthquake since 1830, this work's results is the relative accurate with respect to the results of previous studies.