Assessing the Efficacy of the UV Index in Predicting Surface UV Radiation: A Comprehensive Analysis Using Statistical and Machine Learning Methods
DOI:
https://doi.org/10.12928/irip.v6i2.8216Keywords:
UV Index prediction, Public health implications, UV radiation modelling, Predictive analytics, UV protection strategiesAbstract
The study investigated the relationship between the UV Index and measurements of ultraviolet A (UVA) and ultraviolet B (UVB) radiation to evaluate the effectiveness of the UV Index in predicting and understanding UV radiation at the surface. The implications of this study are significant for public health policies and UV protection strategies. This study used a variety of statistical analyses and modelling techniques, including ANOVA, Naive Bayes classification, decision trees, artificial neural networks, support vector machines (SVM), and k-means clustering, to examine relationships and predict UV Index values. ANOVA analysis showed a significant relationship between the UV Index and UVA and UVB measurements. Prediction models such as Naive Bayes classification, decision trees, and artificial neural networks showed variability in their accuracy. Notably, SVM showed a high degree of accuracy in predicting UV Index values, while k-means clustering effectively clustered the data based on similarities in UV Index and UV measurements. These findings confirm that the UV Index is a reliable indicator for predicting and understanding UV radiation levels at the Earth's surface. This research underscores the importance of developing more accurate and precise UV Index prediction models. Further investigation is essential to understand the temporal variations and environmental impacts on the UV Index, as well as the broader implications of UV exposure on public health. This study lays a strong foundation for the development of early warning systems and more effective UV protection strategies, ultimately improving public health outcomes and safety measures against UV radiation.
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