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Saeed Balyani,
Volume 16, Issue 43 (12-2016)

Knowing of precipitation values in different regions is always of main and strategic issues of human which has important role in short- term and long-term decisions. In order to determine of precipitation model and forecasting it, there are different models, but given that the precipitation data have a spatial autocorrelation, the spatial statistic is a powerful tool to recognition of spatial behaviors. In this research, for determine of precipitation model and predicting of it with geographical factors e.g. altitude, slope and view shade and latitude- longitude by using spatial regressions analysis such as ordinary least squares (OLS) and geographical weighted regressions(GWR), 13 synoptic stations of Khuzestan province from establishment to 2010 were used. Results showed a powerful correlation between precipitations with geographical factors. Also results of modeling through OLS and GWR representative that forecasting of GWR is close to reality, so that in GWR, the sum of errors of residuals is less, the AWT IMAGE is more and there aren't any spatial autocorrelation in residuals and the residuals are normal. The AWT IMAGEof OLS can only justify 75 percent of precipitation variations with spatial factors while in GWR this quantity is 82- 97 percent. Accordingly, it was found that, in east, northeast and north of province the altitudes, in east and northeast and Zagros Mountains the view shade and slope are the most important spatial factors, respectively.

Hossein Asakereh, Robab Razmi,
Volume 18, Issue 50 (3-2018)

In the present study, the main aim was the spatial evaluation summer rainfall of northwest of Iran based on30 stations in northwest of Iran during 30 years of statistical period (1985-2014). An attempt, using geo-statistical modeling by ordinary least squares (OLS) and geographically weighted regression (GWR) procedures, was also made. The results represented that the GWR model with higher S2, lower residuals and lower RMSE is an optimized geo-statistical model for rainfall modeling of this area. This model can explain spatio-temporal rainfall distribution in northwest of Iran in a diversified topographical and geographical background. This model revealed that two spatial factors including elevation and slope, have the most important role in the summer rainfall behavior.Therefore Elevations in the mountainous and eastern parts of Lake Urmia, Latitude in the northern regions and slopes in the east of the region, have the most role in the spatial variations of summer precipitation in northwestern Iran.

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