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.

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A dominant source of error in space-based geodesy is the tropospheric delay, which results in excess path length of the signal as it passes through the neutral atmosphere. Many studies have addressed the use of global weather models and local meteorological observations to model the effects of this error in Global Positioning System (GPS) and Differential Interferometric Synthetic Aperture Radar (DInSAR) data. However, modelling of zenith tropospheric delays (ZTDs) errors in the GPS data, particularly in the areas of strong topographic relief, is highly problematic because ZTD estimates cannot be captured by low resolution weather models and often it is not possible to find a nearby weather station for every GPS station. In this paper, we use DInSAR data with high spatial and temporal resolution from the volcanic island of Hawaii to estimate the seasonal amplitudes of ZTD signals, which then are used to remove this error from GPS data. Here we observe the seasonal amplitude for more than one million DInSAR pixels for the time period between 2014 and 2017 and propose a best-fitting elevation-dependent model. This model is an integration of the exponential refractivity function and is linked to the observations from a radiosonde station and a weather station. It estimates seasonal amplitudes ranging from 0.2 cm at the highest elevations to 5.6 cm at the lower elevations, increasing exponentially from the DInSAR reference elevation. To demonstrate the potential of this model for correction of GPS data, we compare the modelled seasonal amplitude to the observed seasonal amplitudes of the variation of the local ZTD, computed from the Canadian Spatial Reference System-precise point positioning (CSRS-PPP) online application, for 21 GPS stations distributed throughout the island. Our results show that this model provides results with root-mean-square error (rmse) values of less than 1 cm for the majority of GPS stations. The computed rmse of the residuals between the modelled seasonal signal and the high frequency variations of the ZTD signal at each station relative to the reference GPS station, here PUKA, range between 0.7 and 4.1 cm. These estimated values show good agreement with those computed for the rmse of the residuals computed between the observed seasonal signal and the high frequency variations of ZTD, ranging from zero to 0.3 cm. This confirms the potential of the proposed DInSAR model to accurately estimate the seasonal variation of ZTDs at GPS stations at any arbitrary altitude with respect to the reference station.