Comparison of the Performance of Geographically Weighted Regression and Ordinary Least Squares for modeling of Sea surface temperature in Oman Sea

In Marine discussions, the study of sea surface temperature (SST) and study of its spatial relationships with other ocean parameters are of particular importance, in such a way that the accurate recognition of the SST relationships with other parameters allows the study of many ocean and atmospheric processes. Therefore, in this study, spatial relations modeling of SST with Surface Wind Speed (SWS), Chlorophyll a Concentration, latitude and longitude in Oman Sea between 2003 to 2016 was performed by Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) method available in ArcGIS software and the outputs of the two methods were compared. The results of the OLS method showed that the Surface Wind Speed variable had the most effect on estimating SST values in the Oman Sea, and other variables had shown a low effect on the SST estimation. But in the GWR model, it was found that the longitude variable had the most effect in the estimation of SST values and had a positive relation with SST. In this model, the SWS variable has a positive relationship with SST, but its impact is less in compared with OLS model. Other variables also have a negative relationship with SST. Subsequently, using the local explanation coefficient (R2), it was determined that the GWR model had a higher accuracy than the OLS model for estimating SST values in the Oman Sea, so that the GWR model justify 85% of SST spatial changes in the Oman Sea, but the OLS model justifies only 55% of spatial variations of this parameter. The higher accuracy of the GWR model in the estimation of SST values was found in the eastern and western parts of the Oman Sea and this model was less accurate in the central part of the sea.