Prediction of Solar Ultraviolet Radiations Using Random Matrix Theory

Reza  Malekzadeh; Masoud  Seidi; Nikan  Asadpour; Hadi Sabri

doi:10.18502/fbt.v11i1.14505

Reza Malekzadeh Department of Medical Physics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
Masoud Seidi Department of Physics, Faculty of Physics, Ilam University, Ilam, Iran
Nikan Asadpour Department of Medical Physics, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
Hadi Sabri Department of Physics, Faculty of Physics, University of Tabriz, Tabriz, Iran

DOI: https://doi.org/10.18502/fbt.v11i1.14505

Keywords: Random Matrix Theory; Modeling; Environmental Radiation; Ultra Violet.

Abstract

Purpose: The correlation of different samples can be described by analytical models such as random matrix theory. In this study, we tried to describe the correlation of different types of ultraviolet values in different months, weeks, and hours to get a significant relationship of special times, which one needs to get enough intensity of the sun or avoid getting sunburn.

Materials and Methods: To this aim, we focused on the hourly and daily mean amounts of ultraviolet A, B, and C intensities of solar radiation in Tabriz urban area were measured during a full year of 2017-2018. We used such ultraviolet values which are measured at the same hour of the day to satisfy the same symmetry criteria which are necessary in random matrix theory. These data are unfolded and classified in different sequences to analyze in the nearest neighbor spacing distribution framework via the maximum likelihood estimation technique.

Results: Strong correlation is yielded for daily values of UVA in comparison with the other types of ultraviolet radiations. Also, we considered the dependence of correlation degrees of these three types of ultraviolet to average temperature and humidity at different months.

Conclusion: The results propose more correlation of UVA indices in August while such correlation of UVC radiations are yielded in December.