Intelligent analysis of global warming effects on sea surface temperature in Hormuzgan Coast, Persian Gulf

Abstract

As scientific and economic interests in climate prediction and predictability have increased considerably in recent years, a need for global sea surface temperature (SST) prediction for use in global forecasts for climate variability studies have emerged. This paper examines the potential of gene expression programming (GEP) in estimation of SST from global mean temperature (GMT) in comparison with linear regression (LR), polynomial regression (PR) and exponential regressions (ER). In the present study, GMT and SST data for the point with latitude of 26.5°N and longitude of 56.5°E in the coast of Hormuzgan in the Persian Gulf were gathered for the time period of 1985 to 2010. 60% of datasets (1985-2000) were used for training of mentioned models and the residual 40% of data (2000-2010) were used for testing. Finally, the performance of the GEP was compared with regression models using some statistic parameters for error estimation. The results of GEP were satisfactory, with values of the correlation coefficient of (R) of 0.945 and root mean square error (RMSE) of 0.135 for independent test set. The performance of examined GEP method was superior in comparison with regression models that were developed in parallel (R for regression models ranging between 0.330 and 0.623 and RMSE ranging between 0.361 and 0.462). The comparison test results reveal that by using GEP method, SST can be predicted, precisely.