Comparison of Regression decision Tress, Geographically Weighted Regression and Ordinary Least Square to map isohyets

of mentioned methods that use relationship between precipitation and geographically factors. Therefore current study was carried out to compare global regression methods (multi polynomial regression and ordinary least square methods), local regression methods (local polynomial regression, geographically weighted regression) and decision tree regression. Average of 20 years annually precipitation data of 185 meteorological observations over Gilan Province and its neighboring stations was used for modeling of spatial distribution variations of mean annual precipitation by using other variables like elevation and position of points to the sea level. Comparison between results using cross validation technique showed that geographically weighting regression method has the highest accuracy to estimate mean annual precipitation (R2=0.87 and RMSE=147mm) and can be used to map isohyets in Gilan province.