Showing 5 results for Factor Analysis
Mr Ali Hasanzadeh, Mr Hooshmand Ataei, Mr Nader Parvin, Mr Amir Gandomkar,
Volume 6, Issue 1 (5-2019)
Abstract
Agricultural crops have damaged a lot due to the aftermath of late spring frost, and because low temperatures have damaging effects on agricultural production, it is essential to anticipate and prevent potential damages. Often, atmospheric temperature variations are very urgent due to the high temperature of the systems and the plants cannot adapt themselves with severe oscillations and, have been damaged. The aim of this study was to analyze the climate of the spring frost in Kermanshah, identifying the sea level equations and the late spring freezing frost of the period from 1990 to 2015. This survey has been done to determine the period of the freezing phenomenon, determine the minimum daily temperature of 7 stations placed in Kermanshah, Hamedan, and Ilam. After analyzing the data of spring frost freezing of Kermanshah province using the main component analysis technique and hierarchical clustering method, the most common 10 patterns of late spring coldness of the area were studied and determined. In 10 resulting cluster, 8 clusters were related to the high-pressure pattern of Siberia. From the total 91 days of spring frost freezing in Kermanshah province (79% (72 days)) is due to the high rainfall of Siberia, 12% (11 days) is due to the Mediterranean climate and 9% (8 days) is due to the Van lake climate. These pressure patterns were named according to the location of their deposition, which caused the loss of the environment and the freezing frost of the spring.
Khabat Derafshi,
Volume 7, Issue 3 (11-2020)
Abstract
In this study, the risk map as an index to define the said complexity was prepared in 5 categories of risk by combination of Tehran metropolis flood hazard and vulnerability maps. To analyze the risk varieties, the hydrological catchments of Tehran were extracted by Arc Hydro model and 12 catchments were selected. Using land use, roads network, and the percentage of residential floor area, bridges, altitude, slope and drainage density variables, the flood hazard map was calculated. Dilapidated urban blocks, population density, land use, bridges, slope and drainage density layers were used as variables which affecting the flood vulnerability. Covariance index was applied for matched variables and considering the locational coherence between the values of them. Based on the new raster layers, flood risk variability in Tehran metropolis as well as in each of the catchments were analyzed using stepwise regression model. Explanation of locational changes of risk between the catchments needs to calculate the weighted average risk and the independent variables in 12 catchments that obtained by zonal statistics. Based on these average values the factor analysis used to determine the varifactors or main components of the variability in flood risk between the catchments. Finally, fractal geometry models (perimeter-area and cumulative number-area) were used to demonstrate the chaos of the flood risk value in 5 categories of risk. According to the flood hazard zoning map of Tehran metropolitan area, the extent of high hazard zone is 129.6 square kilometers. High risk zone covers 28.6% of Tehranchr('39')s area, indicating that most of the citychr('39')s extents (174.4 square kilometers) are located in the high flood risk zone. After that, the moderate hazard zone is 28.5% of the city area. Very low zones with 3.53% of the total area are the smallest zones in the city, which are only 21.5 square kilometers. Overall, 78.3 percent of the total area of the city is located in the moderate to very high zones of flood hazard, reflecting Tehranchr('39')s challenge to flooding. The vulnerability map defines that 138 km2 of the Tehran city area is located in high and very high zones of the flood vulnerability. According to Tehran metropolitan flood risk zoning map, 163.1 km2 of Tehran city area is located in high risk zone which has the highest rate among flood risk categories in Tehran metropolis (26.9%).
- Mahmoud Roshani, - Mohammad Saligheh, - Bohlol Alijani, - Zahra Begum Hejazizadeh,
Volume 8, Issue 4 (1-2021)
Abstract
In this study, the synoptic patterns of the warm period of the year that lead to the cessation of rainfall and the creation of short to long dry spells were identified and analyzed. For this purpose, the rainfall data of 8 synoptic stations were used to identify the dry spells of the warm season for 30 years (1986 to 2015). The average daily rainfall of each station was used as the threshold value to distinguish between wet and dry spells. Then, according to the effects of dry spells, they were defined subjectively and objectively with different durations. Thus, 5 numerical periods of 12 to 15, 15 to 30, 30 to 45, 45 to 60 and more than 60 days were identified. By factor analysis of Geopotential height data at 500 hPa, 4 components were identified for each period and a total of 20 components for 5 dry spells. Therefore, 5 common patterns control the stable weather conditions of dry spells. Most dry days are caused by subtropical high-pressure nuclei, which have a wide, even, dual-core, triple-core arrangement. The effect of subtropical high pressure on the dryness of the southern coast of the Caspian Sea is quite evident. Other dry days were caused by southerly currents, weakening of northern currents, and the trough Anticyclones’ area. Also, the anomaly map of the components days at the 500 hPa level showed that the anticyclones and cyclones correspond to the positive and negative phases of the anomalies, respectively.
Dr Saeed Jahanbakhsh Asl, Dr Yagob Dinpashoh, Phd Student Asma Azadeh Garebagh,
Volume 11, Issue 2 (8-2024)
Abstract
Evapotranspiration is one of the main elements of hydrologic cycle. Accurate determination of reference crop potential evapotranspiration (ET0) is crucial in efficient use of water in irrigation practices. ET0 can be measured directly by lysimeters or estimated indirectly by many different empirical methods. Direct measurement is cumbersome, needs for more time and costly. Therefore, many investigators used empirical methods instead of direct measurements to estimate ET0. Nowadays, the FAO-56 Penman Monteith (PMF56) method is known a bench mark for comparing the other empirical methods. For example, in the works of Zare Abyaneh et al. (2016), Biazar et al. (2019), Dinpashoh et al. (2021) and Dinpashoh et al. (2011) PMF56 method was used to estimate ET0 and comparing the outputs of other empirical methods. Many researchers analyzed trends in ET0 time series in different sites around the Earth. Among them it can be referred to the works of Sabziparvar et al. (2008), Babamiri & Dinpashoh (2015), Dinpashoh et al. (2021), Dinpashoh (2026) and Tabari et al. (2013). ET0 can be affected by many different climatic factors such as maximum air temperature (Tmax), minimum air temperature (Tmin), relative humidity (RH), wind speed, and actual sunshine hours. Factor analysis (FA) is a multivariate method that reduces data dimensionality. In general, climatic variables have high correlation with each other. On the other hand, these variables affect ET0. The FA can be used to reduce data dimensionality in which correlated variables converted to few uncorrelated factors.
- Ghazale Rafiee, - Aida Maleki, - Yaser Shahbazi, Asghar Molaei,
Volume 11, Issue 3 (12-2024)
Abstract
Natural biological crises are a constant threat to human societies; Therefore, it is considered important to be prepared to control and prevent such disasters. In dealing with such urban crises, addressing the category of non-pharmacological interventions against infectious diseases can be a way forward. Therefore, the aim of the current research is to find the main effective indicators in the epidemic and to explain the environmental components resulting from the interaction and correlation of the relevant measurable indicators at the locality level based on previous environmental studies.
In this research, in order to obtain a community of opinion about effective indicators in epidemiological crises, a systematic search was first conducted using the keyword of pandemic resilience. For this purpose, several widely used databases such as Web of Science, Scopus and Elsevier were searched between 2013 and 2023. After checking the quality of the conducted researches, 42 indicators were selected. Further, in order to find out the underlying variables and identify the basic factors or criteria in order to explain the correlation pattern between the observed variables, the exploratory factor analysis method was used and SPSS version 26 software was used to analyze the data. Data from 118 Tabriz neighborhoods were used for exploratory factor analysis. After collecting the data, the process of change, standardization has been done to prepare the data and convert the raw data into percentage, growth rate, average and ratio. Then, the status of urban resilience components against epidemic diseases was calculated and normalized separately based on factor load for each neighborhood. Finally, the results were illustrated using Arc GIS software.
The findings of the research indicate that 42 indicators affecting the spread of epidemic diseases at the level of localities can be re-categorized into 8 components of accumulation, diversity and design, social factors, density, economic factors, health infrastructure, environmental pollution and green spaces. Also, based on the findings, it can be said that the condition of most of the components in the neighborhoods of Tabriz city is at an average level.
Based on the results, the effects of environmental factors on the transmission of Covid-19 are differentiated spatially. These components represent more than 82% of the changes in effective environmental factors.
