Performance of the AR4 and ATR Models in the Simulation of Climatic Parameters with Artificial Neural Network (Case Study: Watershed Cezar)

In the present study, performance of 6 Atmospheric general circulation models as: CGCM3, HADCM3, CSIROMK3 (collection of AR4 models) and CGCM1, GFDL30, NCARPCM (from the collection of model ATR) for simulation of climatic parameters includes average temperature and precipitation using artificial neural network (ANN) in Caesar basin were evaluated. For training of artificial neural network model was used perceptron forward method. According to performance evaluation of these models using maximum absolute error, mean absolute error, root-mean-square error and Russell Square Quality Representatives of the model, among AR4 and ATR models, AR4 models had better performance than ATR models and less uncertainty in simulating of climatic parameters (rainfall and average temperatures) in Caesar basin at 1996-2000. Between these 6 models, CGCM3 is the best performance in the simulation of climatic parameters for Caesar basin. CGCM3 and HADCM3 models have the lowest differences with observed climate parameters. Also, the results showed that CSIROMK 3.0 and CGCM1 models have the most differences with observed climate parameters