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Hosseini A, Ashtarinezhad E. Predicting the average annual maximum wind speed in Sistan region using spatio-temporal regression method. Journal of Spatial Analysis Environmental Hazards 2022; 9 (2) :141-160
URL: http://jsaeh.khu.ac.ir/article-1-3204-en.html
1- Payame Noor University, Iran. , ahmad_hossayni@yahoo.com
Abstract:   (3079 Views)
Predicting the average annual maximum wind speed in Sistan region using spatio-temporal regression method
: Abstract                              
The wind is a quantitative vector that moves from high-pressure centers to low-pressure centers and is measured by two factors, the direction of the wind, which originates from the north and increases in degrees clockwise, and the wind speed, which is the horizontal flow. Air is measured in units of time. The wind speed can move colloidal particles, including clay and silt, from the site of destruction to a distance of hundreds of kilometers. Studies show that most dust days occur in the eastern regions of the country so that in the range of 120-day winds in Sistan, the frequency of dust per year reaches more than 150 days. Moreover, the prediction of numerical values ​​of maximum annual wind speed using the Spatio-temporal regression method was considered in this study. Error variance and alignment analysis using variance inflation index showed that numerical models of the Spatio-temporal regression of data could predict the Average maximum wind speed in the coming years. The results also show that regression Spatio-temporal until 2022 can predict wind speed.
The numerical model indicates that the lowest annual average wind speed from 2019 to 2022 is related to the Ghaen station. Its forecast trend shows that by 2022, the average annual wind speed will decrease. The highest annual average wind speed is related to Zabol station, in which the forecast trend of this station shows that the average annual wind speed will decrease by 2022.

Keywords: Spatio-temporal regression, Wind speed prediction, Sistan region
 
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Type of Study: Research | Subject: Special
Received: 2021/02/8 | Accepted: 2021/10/25 | Published: 2022/12/1

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