TY  - JOUR
T1  - Prediction of Air Temperature Using Artificial Intelligent Methods
AU - Ali Ghorbani, Mohammad AU - Kazemi, Honeyeh AU - Farsadizadeh, Davod AU - Yousefi, Peyman 
JO  - Journal of Engineering and Applied Sciences
VL  - 7
IS  - 2
SP  - 134
EP  - 142
PY  - 2012
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2012.134.142
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2012.134.142
KW  - Adaptive Neuro Fuzzy Inference System
KW  -air temperature
KW  -artificial neural networks
KW  -genetic programming
KW  -Tabriz
KW  -Iran
AB  - 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.
ER  -