Identification of Variables Affecting Levels of Salt Concentrations in Shatt Al-Arab Water Using Modified Kernel Principal Component Analysis

This study paper is an attempt to bring to light a new approach in the treatment of the Gaussian function. The Gaussian function is considered the basis for building the elements of the kernel matrix within the methodology of the kernel principal components that aims to reduce the dimensions and then determine the most influential variables. Besides, it works on reducing the mathematical complexity that can arise because of multidimensionality, especially if it is the data suffers from a non-linear problem in describing the relationships. This research paper has included processing the introductory parameter matrix (H) by adopting two types of matrices, namely (H¹ diagonal, and H² hybrid diagonal). For achieving the benefit of this paper, it has been applied to the phenomenon of salt concentrations in Shatt al-Arab water in Basra Governorate through a number of climatic variables for identification of the most influential variables. The modified Gaussian function (MGK) was used and compared with the traditional method (TGK) by adopting two methods of estimating the introductory parameter matrix H. The simulation results brought to light that the (MGK) could not achieve better results than the (TGK) for any type of matrices (H¹ & H²), estimated by the two methods (NS-R, ROT). Despite this fact, the (MGK) and (TGK) were consistent in determining the climatic variables most affected by the rise in salt concentrations when adopting the (NS-R) method, which are (air temperature, minimum temperature, maximum temperature, and solar brightness). While when adopting the (ROT), the (MGK) determined other variables, while the traditional method identified the same variables mentioned above.