Showing 3 results for Safarian
Mr Vahid Safarian Zengir, Dr Batol Zenali, Mr Yusuf Jafari Hasi Kennedy, Miss Leyla Jafarzadeh,
Volume 5, Issue 2 (9-2018)
Abstract
Investigation and evaluation of dust and microstrip phenomena is one of the important values in the management of climate and environmental hazards in the Middle East, especially in the arid, western, southern and central parts of Iran. Methods and plans for studying this phenomenon and its management are of great importance and great value. According to studies on dust phenomena based on predictive methods with low error, contradictory and low, the evaluation of the characteristics of dust and its prediction will reduce the irreparable damage that results from it. To do this, in this research, dust monitoring and assessment of its prediction in Ardebil province was performed using the ANFIS model. The data used in this study is the amount of dust in the relevant statistical period to each station from its inception until 2016. The dust phenomenon was used in the observed and predicted time intervals to assess the dust and the ANFIS model for predicting dust phenomena. According to the findings of this study, in the monitoring and prediction of dust situation, the frequency of occurrence in observed years in the maximum amount of dust in Ardabil station with 74% and the lowest in Mashgin is 8%. In the years to come, the maximum amount of dust at Khalkhal Station was 61.67% and the lowest was 10% in Mashgin. In terms of amount of dust, the Ardebil station is more intense than the rest of the stations. In terms of the severity of drought that has been studied, each of the 5 stations studied has a dust concentration of more than 74%. For the 5 stations studied for the next 18 years using manually generated codes, the stations were divided in time series, with the highest average error of training at Pars-Abad Moghan Station with 0.091% and less The highest value was obtained at the Grammy station with a value of 0.001%.
Sir Vahid Safarian Zengir, Sir Behroz Sobhani,
Volume 5, Issue 4 (3-2019)
Abstract
Introduction
Changes, although low in temperature, change the occurrence of extreme phenomena such as droughts, heavy rainfall and storms (Varshavian et al., 2011: 169). Reducing the daily temperature variation has led to a reduction in the frequency of occurrence of temperature minima, especially in winter (Schiffinger et al., 2003, p. 51-41).
Material and method
The purpose of the present study was to investigate and predict the risk of monthly rainfed temperatures on horticultural and agricultural products in northern Iran. For this purpose, first, the data of the temperature of the whole station were obtained at a time interval of 30 years. Then, using Anfis's adaptive neural network model, data were collected for prediction and prediction for the next 6 years. Then, to measure the land suitability of the northern strip Iran was used for cultivating according to the predicted data using two models of Vikor and Topsis.
Conclusion:
In recent years, damage to agricultural and horticultural products has been increased due to temperature fluctuations. Accordingly, in this research, the prediction of the risk of monthly rainfed temperatures on horticultural and agricultural products in northern Iran has been investigated. Based on the predicted data, the minimum temperature of the Gorgan station was the lowest educational error with a value of 0.10 and at the maximum temperature, the lowest error was 0.015. Finally, in Golestan province, the maximum temperature And at least both are weak in the incremental state. Minimum and maximum temperature of Bandar Anzali station was the lowest educational error with the value (0.013, 0.10). In Gilan province, the maximum temperature peaks and at least both are in incremental conditions and the maximum temperature has a higher intensity. Be The minimum temperature of the Babolsar station was the lowest educational error with the value of 0.019 and at Ramsar maximum temperature, the lowest error was 0.016, and Mazandaran province exhibited maximum and minimum temperatures at both incremental and minimum levels Temperature showed greater intensity.
Results:
According to the findings of the study, with respect to the friction frain modeling, the maximum temperature showed the lowest defect compared to the minimum temperature. In Golestan province, the maximum temperature peaks and at least both are in weak increment, but in Gilan province, the maximum temperature peaks and at least both the maximum and maximum temperatures are higher. Mazandaran province showed maximum temperature and minimum temperature in both incremental and minimum temperature conditions.
Dr Vahid Safarian,
Volume 8, Issue 4 (1-2021)
Abstract
Objective: This study aims to analyze greenhouse gas variations across Iran and to identify the gases that exert the greatest influence on their overall dynamics. The findings enhance understanding of atmospheric pollution patterns and support the development of effective mitigation strategies. These results provide a scientific basis for climate-change mitigation planning in Iran. The study relies on satellite-based remote sensing datasets.
Methods: This study analyzes the temporal and spatial variations of major greenhouse gases including carbon monoxide, nitrogen dioxide, ozone, water vapor, and methane across Iran from 2019 to 2024. Sentinel-5P satellite data were extracted via the Google Earth Engine platform, and after filtering and removing low-quality observations, the data were standardized using the Z-Score method to enhance comparability and correlation analysis. Principal Component Analysis (PCA) was applied to reduce data dimensionality and identify dominant variation patterns. Temporal and spatial trends were then quantified using complementary statistical techniques.
Results:
Methane exhibited a consistent increasing trend from late 2021 through 2024 and accounted for the largest share of total variance (R² = 0.87), likely reflecting intensified anthropogenic activities and regional climatic shifts. CO, NO₂, and O₃ were mainly affected by seasonal fluctuations and nonlinear factors, and no clear long-term increasing or decreasing trends were observed. Water vapor showed a direct relationship with temperature variations, water sources, and atmospheric patterns, with its lowest concentrations recorded during the cold months and increases observed in the warm months. PCA analysis indicated that the first two principal components explained more than 70% of the total data variance, with CH₄, O₃, and NO₂ contributing the most to the overall variations.
Conclusions: The study results indicated that greenhouse gas variations in Iran are simultaneously influenced by natural factors and human activities. The combination of satellite data, statistical analysis, and PCA enabled a precise assessment of the temporal and spatial trends of greenhouse gases, providing valuable information for planning pollutant reduction and developing strategies to combat climate change.
