Predicting Last Spring Freezing Day in West and Northwest of Iran - Hasan Zolfaghari; Gholamreza Zahedi; Tayebe Sajjadifar (جغرافیا و پایداری محیط)
In this paper we try to predict last freezing day of spring for 17 meteorological stations in west and northwest of Iran by Artificial Neural Networks. The method used to do so was the back propagation method. Climatologic data included first day of freezing, minimum absolute temperature in the last day of freezing, humidity at 3 o’clock (GMT) in the last day of freezing, average relative humidity in the last day, average pressure in the day before last day and cloudiness in the last day of freezing input the network, and the last day of freezing was output. Artificial neural networks could predict the last day of freezing for all 17 stations with an accepted error. Biggest errors in this work belong to Arak station with 1.1142 % and smallest errors belong to Mahabad station with 0.254 %. It was concluded that zonation of the religion based on accomplished prediction exposed effects of the both height and topography.