Assessing the Application of Artificial Neural Networks and SDSM Models to Simulate the Minimum and Maximum Temperatures at Isfahan Station

With regard to climate change and global warming, predicting maximum and minimum temperatures, as one of the most important climate parameters, provide planners with the opportunity to plan and take the necessary measures when needed. In this research, the SDSM and ANN methods were used to model and simulate the maximum and minimum temperature data at a climatological station in Isfahan. The NCEP center data were used as predictor variables to calibrate and evaluate the model. Also, HadCM data were used under the two scenarios A2 and B2 to simulate the minimum and maximum station temperatures in Isfahan during the periods of 2020-2040, 2041-2070 and 2071-2099. The results showed that the maximum and minimum temperatures will increase significantly. Indeed, as compared to the base period, until 2099, the average annual minimum and maximum temperatures will rise for 2 or 3 degrees centigrade in both models based on scenario B2.