TY  - JOUR
T1  - Modeling Monthly Mean Maximum Temperature Using Genetic Programming
AU - , S. Shahid AU - , M. Hasan AU - , R.U. Mondal 
JO  - International Journal of Soft Computing
VL  - 2
IS  - 5
SP  - 612
EP  - 616
PY  - 2007
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2007.612.616
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2007.612.616
KW  - Climate prediction
KW  -monthly mean maximum temperature
KW  -time series modeling
KW  -genetic programming
KW  -artificial neural network
AB  - Climate is a continuous, data-intensive, multidimensional, dynamic and chaotic process. Conventional classical methods, generally used for prediction from historical time series data, often fail to predict climate reliably. Recently, various soft computing techniques are being used for their prediction. Genetic programming has been used in this study for the modeling of monthly mean maximum temperature. The result is compared to that obtained by using neural network. The study shows that the model produced by genetic programming can be used for the reliable prediction of monthly mean maximum temperature of the area. Though the result obtained by genetic programming is more erroneous compared to neural network, it provides an equation that could be used for reasonable prediction of monthly mean maximum temperature manually.
ER  -