International Journal of Soft Computing
ISSN: OnlineISSN: Print 1816-9503
Abstract
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.
How to cite this article:
S. Shahid , M. Hasan and R.U. Mondal . Modeling Monthly Mean Maximum Temperature Using Genetic Programming.
DOI: https://doi.org/10.36478/ijscomp.2007.612.616
URL: https://www.makhillpublications.co/view-article/1816-9503/ijscomp.2007.612.616