Prediction of Air Temperature Using Artificial Intelligent Methods

Estimation of air temperature is one of the important problems in agricultural planning also in water resources management which can be done by using different empirical, semi-empirical and intelligent methods. In the present study, Adaptive Neuro Fuzzy Inference System, artificial neural networks and genetic programming are used to estimate maximum, minimum and mean air temperature values in the synoptic station of Tabriz city, Northwest Iran. Considering the statistical indices, in spite of some very slight differences in the accuracy and error of the models, all three models are able to accurately estimate the minimum, mean and maximum air temperature. Also, explicit solutions that show the relation between input and output variables are presented based on genetic programming. This adds to the superiority of genetic programming over the other two models.