Showing 4 results for yousefi
Homa Dorostkar Gol Khili, Yadollah Yousefi, Mehdi Ramezanzadeh Lasboyee, Hematollah Roradeh ,
Volume 2, Issue 4 (1-2016)
Abstract
Natural disasters is one of the main challenges for developing countries, which not only cause death and emotional pain and suffering of survivors, but greatly affecting development. Reduction programs and prevention of disasters, including policies that countries to increase community capacity in disaster, are followed to improve the effects of these disasters. One of the risks that affect Iran, is flooding. Iran has a very high risk of flooding, which in most years, about 70% of annual credit plan is paied to reduce the effects of natural disasters. Floods in recent years has left a lot of damage in many parts of Iran. Because the flood event and can not be prevented, but we can assess the resiliency and vulnerability of risks to reduce the effects of flooding greatly. Planning in disaster management process can reduce the risks of accidents and improve the resilience. Thus, how and by what means we can increase the capacity of society to accept a certain level of risk is very important. In recent years, many researches, focused over concept of resilience and disaster risk reduction policy. This research study area is the Nekarud basin in Mazandaran province. Population growth and unethical uses of Nekarud and natural resources, humans and their facilities, infrastructure and natural resources of the basin are vulnerable. The aim of this study was to evaluate the resiliency and identify strengths and weaknesses in the flood affected villages Nekarud margin is based on random sampling of villages (8 villages) have been affected by floods in recent years, were selected. The research method is descriptive and analytical study of its nature. The aforementioned villages to assess the resilience, the four dimensions of economic, social, and institutional infrastructure based on the location of the axis (DROP) provided by Cutter and his colleagues in 2008, was used. According to the surveys and the results obtained, it can be stated that the model DROP, because of the location-based (geographic), and the integrity of the elections aspects and indicators to measure and assess the resilience of settlements is a good model. The dimensions considered to measure resilience include: economic, social, institutional and infrastructure. After determining the dimensions required components and indicators research, scientific references were identified by the study, questionnaires were prepared. Secondly, the need of the rural sample in the form of a questionnaire, collected and analyzed after coding in SPSS. The findings of the study showed that the settlements are in a different situation in terms of resilience in different dimensions. The economic resilience for the total sample is 8.96. The amount of this variable for Zarandin-e Olya, Zarandin-e Sofla, Abelo and Kuhsarkadeh rural settlements is higher than the average whole.
Nader Shohani, Lotfali Kozegar Kalj, Sajad Darabi, Said Yousefi Babadi,
Volume 9, Issue 1 (5-2022)
Abstract
Pandemic Covid-19 (Corona); Tehran's resilience against it
Nader Shohani; Assistant Professor, Department of Geography and Urban Planning, Payame Noor University. Tehran Iran
Lotfali College Potter; Associate Professor, Department of Geography and Urban Planning, Shahid Beheshti University, Tehran, Iran
Sajjad Darabi; PhD Student, Department of Geography and Urban Planning, Shahid Beheshti University, Tehran, Iran
Saeed Yousefi Babadi; PhD student, Department of Geography and Urban Planning, Shahid Beheshti University, Tehran, Iran
Abstract
One of the dangers that has caused cities to face a serious crisis is the outbreak of Covid-19 disease. The corona pandemic has taken cities out of their normal routine. Therefore, cities seek to return to their past conditions and urban resilience as soon as possible. Research Method In this descriptive-analytical study, using field survey, four economic, social, managerial-institutional and infrastructural dimensions in the form of 29 items have examined the resilience of Tehran against Corona pandemic. In research, support and advocacy for affected businesses, insurance coverage, support for affected manufacturing sectors, are in the most unfavorable situation. The results obtained from the final table of Vikor technique show that the economic index with a score of 1 is the most important component of resilience against coronavirus, which is lower than other components of resilience. After that, the managerial-institutional component with a score of 0.94 and the infrastructure component with a score of 0.92 in the next ranks are the most important components of Tehran's resilience against coronavirus. The results show that the metropolis of Tehran is not in a favorable position in relation to the corona virus and is not resilient to selected indicators and the economic index has the most impact and the social index has the least impact on the resilience of Tehran.
Keywords: Urban Resilience, Covid 19, Pandemic, Tehran
Pandemic Covid-19 (Corona);
Tehran's resilience against it
Dr Ebrahim Yousefi Mobarhan, Dr Mansor Ghodrati, Dr Mohamad Khosroshahi,
Volume 9, Issue 4 (3-2023)
Abstract
In the study of the trend of dust storm index, the results showed that the study period of 2003-2007 in Semnan province has an increasing trend and has shown significant changes in the 95% confidence range, but the lack of significant changes in the last decade shows the effects of various events. In cross-cutting decisions in the field of dust in the region. The zoning of the DSI index changes in different regions of the province in a 15-year statistical period indicates that from the west to the east of the province due to the increase in the frequency of stormy days with moderate dust (MDS), dust has increased. The correlation between drought and DSI index in Semnan province showed that although DSI index increased during the period under analysis with increasing drought intensity and its correlation with drought during the 15-year period was not significant, but the pattern of DSI index is consistent with It is the pattern of the drought process. According to the results, it can be acknowledged that the dust situation has always been affected by climate, but the relationship between drought and the DSI index has always fluctuated with respect to droughts and wetlands. However, different climatic parameters are different and their impact is different. In addition to human activities, the main role of wind in the amount of dust or the existence of another source of dust should be considered.
Mrs Ziba Yousefi, Dr Hossein Jahantigh, Dr Farhad Zolfaghari,
Volume 10, Issue 4 (12-2023)
Abstract
Investigation and monitoring of desertification in arid and semi-arid regions is a major concern for societies and governments due to its increasing rate. It is essential to identify areas at risk of desertification to manage and control this phenomenon in the shortest possible time and at minimum cost. The objective of this study is to create a map of desertification intensity in the MoradAbad plain of Saravan using the Albedo-NDVI model, which is based on remote sensing. Two Albedo and NDVI indicators were extracted from Landsat 8 satellite images in Erdas Imaging software after necessary corrections. A linear regression was formed between the two indicators by selecting 200 pixels corresponding to each indicator. Based on the slope coefficient of the line obtained from linear regression, the equation for determining the intensity of desertification was obtained. A map of the intensity of desertification was prepared based on Jenks’ natural refractive index. To evaluate the accuracy of the model, a clutter matrix was formed between 100 corresponding points. The results of linear regression between NDVI and Albedo indices showed that these two indices have a high negative correlation with each other (R = -0.85). The results of the desertification severity classification based on this model showed that 35% of the area is in the very severe class and only 5% of the area is without degradation. The model’s accuracy value was obtained with a kappa coefficient equal to 0.58, indicating good accuracy of the model.
