Tayebeh Sadat Sohrabi; Abolfazl Ranjbar Fordoie; Abbasali Vali; Seyed Hojat Mousavi
Volume 26, Issue 3 , September 2019, , Pages 689-703
Abstract
Iran is frequently exposed to local and synoptically dust storm due to the geographical location of Iran. In recent years, dust storm frequencies and intensities have been increased significantly in Iran and especially in Isfahan Province, seriously disrupting human life and affecting the quality of ...
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Iran is frequently exposed to local and synoptically dust storm due to the geographical location of Iran. In recent years, dust storm frequencies and intensities have been increased significantly in Iran and especially in Isfahan Province, seriously disrupting human life and affecting the quality of life. This phenomenon is particularly increased in the spring and summer. Climate factors play an important role in dust storms. In this research, spatiotemporal changes of climate factors and dust storms were studied. Therefore, we analyzed climate factors (precipitation, temperature, wind speed and humidity) and dust storms frequency during 1992 to 2016. Poisson regression model was used for statistical modeling of temporal and spatial variations of dust and climatic parameters. According to the models, there was conformity between the results and the predicted values throughout the months. In addition, the results showed that wind speed played a major role in the occurrence of dust storms and had the highest coefficient. The results also showed that most of the dusty days are in the spring and then in the eastern part of the province, which is related to the local centers in the eastern part of the province and summer winds.
Mohsen Yousefi; leila kashi zenouzi
Volume 22, Issue 2 , August 2015, , Pages 240-250
Abstract
The aim of this study was to determine some factors affecting dust storms phenomenon using different methods. In order to determine the best-input combination, variable reduction techniques such as factor analysis (maximum likelihood, principal component analysis), Gama test, and multivariate forward ...
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The aim of this study was to determine some factors affecting dust storms phenomenon using different methods. In order to determine the best-input combination, variable reduction techniques such as factor analysis (maximum likelihood, principal component analysis), Gama test, and multivariate forward regression analysis were used. Each of these methods presented different combinations used by feedforward neural network model, with Levenberg–Marquardt algorithm and multivariate forward regression with R²=0.87 and RMSE=0.04 was selected as the best suitable combination of neural network model. In addition, monthly and seasonal data were applied by neural network using the best-input combination, and the simulation of dust storm phenomenon was done in summer and spring during the months of April, May, June, July, August and September with a higher correlation coefficient and lower mean square error, due to the good distribution of the dust storm data. The results showed that based on these methods used in this study, dominant wind speed, horizontal visibility, continuity and average of wind speed were the most important factors affecting dust storm phenomenon in Yazd province.
Mohammad reza Jamalizadeh Tajabadi; Ali reza Moghadam nia; Jamshid piri; Mohammad reza Ekhtesasi
Volume 17, Issue 2 , September 2010, , Pages 205-220
Abstract
Dust storms are common climatic events in arid, semi arid and desert regions of the world. These events impact human resources by foundation losses, every year. Accurate prediction of these events can be effective for decision support in environmental, health, army, and other related fields. An artificial ...
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Dust storms are common climatic events in arid, semi arid and desert regions of the world. These events impact human resources by foundation losses, every year. Accurate prediction of these events can be effective for decision support in environmental, health, army, and other related fields. An artificial neural network is a method which can predict nonlinear problems. In this study we attempted to predict dust storms and low visibility in Zabol city using synoptic data. Result indicates that this method is somewhat successful and appears that via identification of much more dust storm occurrence process, we can do more accurate prediction.