Routing and Seasonal Precipitations Analysis in Germi County, Using Regression Model for Time Series with Seasonal Changes

One of the most important factors in the structure of the Earth is climate. Without a doubt human nature and all life aspects are affected by weather conditions. Based on available evidence, the warm area has been exposed to amounts of rainfall. Therefore, the rainfall data during 1999 to 2011 in weather stations city-scale seasonal warmth of the hot weather has been obtained and after the pre-processing, the linear and nonlinear trends have been used for seasonal and annual changes and in order to reveal the fluctuations of seasonal-annual rainfalls and apply all graphic and mathematic methods and available functions in the Excel and SPSS software, the rainfall element was used. Based on the linear and non-linear equation (Sixth Time Poly Nomial Model) of the annual rainfall changes, the findings have shown that the determination coefficient (R2) is equal to 0.01 and there is a 0.01 coefficient between the time and the rainfall. The determination coefficient (R2) of Sixth Time Poly Nomial Model is 0.24 and it indicates that 24% of the rainfall changes are explained by the time changes and there is a 48% coefficient between the rainfall changes and time. The results indicated that Sixth Time Poly Nomial Model for the annual rainfall, because of having higher amounts of determination coefficient (R2), is more suitable than the linear Model.