مهدی فرخ اناری

The extended breadth, brilliant historical background and powerful ancient empires lead researchers to conclude that Iran must have been powerful enough in those eras in scientific domains especially in terms of geographical knowledge. But the remaining scientific works in geographical arena isn’t considerable enough. In the present study, using the literature review method, an attempt has been made to reveal few geographical references about Iran prior to the advent of Islam. These references are books, manuscripts, inscription and epopee forms. Among the books, the Avesta, Bundahišn, Dēnkart, and Menog-i Khrad have a significant importance. Moreover, Šahrestānīhā ī Ērānšahr, The wonder and remarkability of Sagastān, History of Karkā ḏe Bēṯ Selōḵ, The Chronicle of Arbela, Parthian Stations and Movses Khorenatsi are geography books that too discuss geographical issues of ancient Iran. The letters of ancient kings such as Ardashir-e Babakan, Tansar and Artabanus to Susa citizens also contain information about geography. Some inscriptions from ancient Iran have geographic concepts such as Darius, Persepolis, Susa, Xerxes I and Shapur I. There is a lack of geographers’ name in ancient Iran same as scientific references about geography. Only two people named Isidore Charax and Movses Khorenatsi could be found who studied the Iranian geography, directly.

Natural and human factors are always a threat to the destruction and damage of ancient monuments, and depending on the type of area, each of these factors can be damaging; many historical sites have been destroyed or seriously damaged as a result of these circumstances. Using GIS, the threatened regions of historical sites in Lorestan's Khorramabad region were identified to take action to preserve them. Shapur Khast Fortress (Falakol aflak), Broken Bridge, Safavid Bridge (Gap Bridge), brick minaret, Khorramabad inscription, and Gerdab Sangi are among the region's known ancient sites. Effective factors in the destruction of ancient monuments are five criteria defined by professionals in the field, which include the distance from the waterway, distance from the streets, slope, height, and slope orientation, all of which are depicted on a geographical map. The model is next assessed utilizing modeling by fuzzy logic operators approach and identifying the effect of each criterion. The final result includes five vulnerability classes: very high, high, medium, low, and very low. In this study, the AND operator provides the optimal risk-adjusted state, which has been proven by empirical verification and historical sources.

The Coronavirus disease (Covid-19) is one of the infectious and contagious ones called 2019-nCoV acute respiratory disease. Its outbreak was first reported on December 31, 2019, in the Chinese city of Wuhan that quickly spread throughout the country within a few weeks and spread to several other countries, including Italy, the United States, and Germany, within a month. This disease was officially reported in Iran on February 19, 2020. It is important to detect and analyze high risk zones and establish regulations according to the data and the analyses of Geographic Information System (GIS) in epidemiological situations. Meanwhile, the GIS, with its location nature, can be effective in preventing the breakdown of Covid-19 by displaying and analyzing the dangerous zones where people infected with the disease. In fact, recognizing regions based on the risk of getting the disease can influence social restriction policies and urban movement rules in order to prepare daily and weekly plans in different urban regions. In this applied and analytical research, high and low risk zones of Tehran have been identified by using the random forest algorithm which is used for both classification and regression. The algorithm builds decision trees on data samples and then predicts data from each of them, and finally chooses the best solution. In this research, 7 effective criteria have been used in the level of risk of regions toward Covid-19 virus, which is: subway paths and bus for rapid transits, hospitals, administrative and commercial complexes, passageways, population densities and urban traffic. After providing the map of high-risk zones of Covid-19, the Receiver Operating Characteristic curve (ROC) has been used for evaluation. The area under the curve (AUC) obtained from ROC shows an accuracy of 98.8%, which means the high accuracy of this algorithm in predicting high and low zones toward getting the Covid-19 disease.