TAYYEBEH MESBAHZADEH; Ali Salajegheh; farshad soleimani sardoo; Gholamreza Zehtabian; Abbas Ranjbar; Mario Marcello Miglietta
Abstract
Today, the phenomenon of dust is known as one of the most important natural disasters in arid and semi-arid regions. The long-term effects of this phenomenon on the human health index are referred to as chronic disease. Therefore, studying and identifying the patterns and centers of this phenomenon seems ...
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Today, the phenomenon of dust is known as one of the most important natural disasters in arid and semi-arid regions. The long-term effects of this phenomenon on the human health index are referred to as chronic disease. Therefore, studying and identifying the patterns and centers of this phenomenon seems necessary in these areas. In this study, in order to simulate the dust emission flux to determine the internal and external critical centers in the central plateau of Iran, WRF-Chem model and GOCART wind erosion scheme and storm were used from July 19 to 21, 2015. The results showed that the Arabian deserts in Saudi Arabia, the deserts of Iraq, as well as the Gharegham desert in Turkmenistan and the Helmand region in Afghanistan are among the most important foreign crisis centers affecting Iran's central plateau atmosphere. Also, the Central Desert (Dasht-e Kavir) has been identified as the main source of dust and the southern parts of the Central Loot Basin and the Jazmourian Basin have been identified as the internal sources of dust. The results also showed that in the Central Loot basin, the amount of 6900 micrograms per square meter of dust increases per second due to the erosion conditions.
TAYYEBEH MESBAHZADEH; Zahra Ayazi; farshad soleimani sardoo
Abstract
By identifying the removal areas the reasons can be identified instead of addressing the causes, And focused on executive activities in the harvesting areas And for this, the identification of sediments is particular importance In this paper, with the aim of better understanding and interpreting sedimentary ...
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By identifying the removal areas the reasons can be identified instead of addressing the causes, And focused on executive activities in the harvesting areas And for this, the identification of sediments is particular importance In this paper, with the aim of better understanding and interpreting sedimentary samples, Investigating and analyzing the distribution of sediments in the facies of the study area has been investigated. For this purpose, after sampling of surface soil and preparation of samples, The drying procedure was performed according to A.S.T.M standard in 8 classes, which was smaller than 64 microns to 4000 microns. By entering the data into Gradistat software, statistical parameters such as mid-diameter, skewness and sample sorting were calculated based on the Fulc's comprehensive drawing method. The results of the study showed that the particle sorting is between 0.8 and 0.3, which confirms the near-average spacing distance from the harvesting area to the sediment accumulation point. The results of the particle tilting index are in the median vein facies, agricultural lands, agriculture, and sandy areas with symmetrical pebble cover. In other facies, the index is tilted towards fine particles. In the facies of the Rigi plain, the puffy lands and the permafrost shells of the wear coefficient classes are between 0-200 and then fully angled and the particles are transported from a distance. In the rest of the facies, the wear coefficients are between 200 to 400, in which case the particles are semi-angled and the particles are transported from a relatively distant distance.
TAYYEBEH MESBAHZADEH; ali azareh; Elham Rafiiei sardooi; Fateme FarzanePei
Abstract
Soil moisture, as the soil hydrologic parameters, can be affected by soil temperature and controls various hydrological processes. Given the importance of this issue, in this study, the efficiency of artificial neural network was studied to simulate soil temperature at 5- 100 cm depth. Recorded meteorological ...
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Soil moisture, as the soil hydrologic parameters, can be affected by soil temperature and controls various hydrological processes. Given the importance of this issue, in this study, the efficiency of artificial neural network was studied to simulate soil temperature at 5- 100 cm depth. Recorded meteorological parameters in the Isfahan synoptic station were used to simulate the soil temperature at different depths. The structure of the neural network was formed with an input layer, a hidden layer and an output layer and network training was done by Levenberg–Marquardt algorithm. Also test and error was done to determine a number of suitable neurons in hidden layer. The results showed that error in both neural network and ANFIS model increases with depth increase that can be due to the weak correlation between soil temperature changes in the lower layers and climatic parameters.