The study investigates the ability of thermal remote sensing in identification of seismic active areas. The thermal characteristics of Land Surface Temperature (LST) represent changes before strong earthquakes. The objective of this research is to find the relationship between earthquake active areas and thermal properties of earth surface. Landsat ETM+ images and earthquake points from USGS have been used as the material of the study. LST have been calculated by the image data and magnitudes of earthquake activity have been interpolated by the seismic point data. The study has found that the areas with higher LST values in long period have more seismic activity on central Alborz Mountain Range in Iran and that the areas along the major faults also have higher LST values.

Abstract:Great earthquakes cause huge damages to human life. Street networks vulnerability makes the rescue operation to encounter serious difficulties especially at the first 72 hours after the incident. Today, physical expansion and high density of great cities, due to narrow access roads, large distance from medical care centers and location at areas with high seismic risk, will lead to a perilous and unpredictable situation in case of the earthquake. Zone # 6 of Tehran, with 229,980 population (3.6% of city population) and 20 km2 area (3.2% of city area), is one of the main municipal zones of Tehran (Iran center of statistics, 2006). Major land-uses, like ministries, embassies, universities, general hospitals and medical centers, big financial firms and so on, manifest the high importance of this region on local and national scale. In this paper, by employing indexes such as access to medical centers, street inclusion, building and population density, land-use, PGA and building quality, vulnerability degree of street networks in zone #6 against the earthquake is calculated through overlaying maps and data in combination with IHWP method and GIS. This article concludes that buildings alongside the streets with high population and building density, low building quality, far to rescue centers and high level of inclusion represent high rate of vulnerability, compared with other buildings. Also, by moving on from north to south of the zone, the vulnerability increases. Likewise, highways and streets with substantial width and low building and population density hold little values of vulnerability.

This article describes a bi-level mixed-integer quadratic problem on a healthcare network in which a defender protects clinics, hospitals, and medical centers concerning a given defensive budget for minimizing the transportation costs, and also the assignment of patients based on their initial capacity before and after interdiction operations. On the other hand, the attacker's subproblem involves the solution of an interdiction hierarchical problem with a total budget constraint on the disruption of the clinic, hospital, or medical center and the remaining capacity such that the total postattack costs are maximized. This problem is modeled as a bi-level problem and the attacker and defender play a Stackelberg equilibrium strategy. An effective and fast hybrid approach of a genetic algorithm (GA), a simulated annealing (SA), and an exact algorithm was presented to solve the model. A realistic case study in the healthcare network is proposed to show the application of the new mathematical model and the effectiveness of the presented approach. The results are analyzed in comparison with GA and SA. Computational results indicate the superiority of hybrid algorithm against GA and SA based on accuracy and running time.

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