Mahdis Yarmohamadi; Ali Asghar Alesheikh; Mohammad Sharif
Abstract
Dust storms are natural disasters that have severely affected human life and the environment. The majority of research in dust storm has been dedicated to the forecasting of storm-prone areas. However, developing models to predict the movement of these storms plays a significant role in the prevention ...
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Dust storms are natural disasters that have severely affected human life and the environment. The majority of research in dust storm has been dedicated to the forecasting of storm-prone areas. However, developing models to predict the movement of these storms plays a significant role in the prevention and management of dust storms, because they reveal the transport pathway and identify the next vulnerable areas against the storm. In this research, a hybrid convolutional neural network (CNN) model has been developed to predict the path of dust storms based on airborne optical depth (AOD) data of MERRA-2 product for the next 12 hours. 40 storm events including 2489 storm hours in a dry region in Central and South Asia have been used for training the model. The results show that the proposed model provides an accurate prediction of the storm's path, so that for the time steps of 3, 6, 9, and 12 hours, the overall accuracy values are 0.9806, 0.9810, 0.9813, and 0.9790, respectively, the F1 score values are 0.8490, 0.8524, 0.8530, and 0.8384, respectively, and the Kappa coefficient values are 0.8387, 0.8424, 0.8431, and 0.8273, respectively.
F Shafiei; A Darvishi Boloorani; S Pourmanafi; A Shahsavani
Volume 8, Issue 2 , November 2016, , Pages 1-16
Abstract
Dust storms are atmospheric phenomena with negative environmental effects and especially for human health. Sampling and analysis of physical and chemical composition of recent dusts show that they are not merely composed of dust, gravel, sand and salt particles, rather they are of complex combination ...
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Dust storms are atmospheric phenomena with negative environmental effects and especially for human health. Sampling and analysis of physical and chemical composition of recent dusts show that they are not merely composed of dust, gravel, sand and salt particles, rather they are of complex combination of chemical elements. Metals such as soil alkaline metals, carbon, silica, aluminum, potassium, calcium and other organic components are observed that all these elements can have harmful effects on public health. In this study, Ahwaz where during last decades has seen several storms was studied. Using collected samples in the ground station and laboratory analyses dust contents were determined for seven dust events. For use of remote sensing technology using satellite images in order to identify the elements of dust, MODIS satellite images were used. Using MODIS images, the least squares method and cross-validation modeling, the relationship between MODIS bands and the results and measured chemical contents of dusts were created. Results show that silica, can be estimated from the ratio of 21 band to 26 with RMS 1.28. For aluminum, the ratio bands 25 to 26 with RMSE 2.08, for calcium the ratio of bands 24 to 25 with RMSE 2.3, for sodium of the ratio of bands 23 to 27 with RMSE 0.48 and finally for Magnesium the ratio of bands 15 to 24 with 0.78 RMSE are useful indexes to identify these elements using MODIS satellite images. According to the results MODIS images are useful for dust storms chemical elements estimation. Also using CALIPSO data, the rate of concentration and intensity of dust particles at the height of 6 km/asl are
Ehsan Tamassoki1; Asadollah Khoorani; Ali Dervishi Bolorany; Ahmad noheghar
Volume 7, Issue 4 , November 2015, , Pages 27-44
Abstract
Wind erosion and dust storms are of major environmental hazards all around the world. Because of The extent of arid and semiarid regions in South and South- East of Iran and the successive incidence of this phenomenon in this region, it is important to study these phenomena. The aim of this study is ...
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Wind erosion and dust storms are of major environmental hazards all around the world. Because of The extent of arid and semiarid regions in South and South- East of Iran and the successive incidence of this phenomenon in this region, it is important to study these phenomena. The aim of this study is monitoring and predicting dust storms in south and south-east of Iran. For this purpose 92 Images of MODIS sensor as well as weather data of 18 stations are used. Dusty days (originating in outside and around the station) were extracted. After monthly and annually monitoring of storms, in order to predicting the frequency of dust storms based on spatial regression, climatic factors and NDVI are used. The results show that the number of storm are high in the beginning year and is decreasing in Jun and July. More than 78 percent of dust storms are of near station type. Spatial regression equations could predict amount of storms. Based on the origin of dust storms in this study combating desertification and wind erosion program could reduce frequency of this storms.
