1 Research Group Solar and Wind Feasibility Technologies (SWIFT),  
Electromechanical Engineering Department, Universidad de Burgos, Escuela Politecnica Superior,  
9000 Burgos, Spain: [EMAIL_ADDRESS] (S.G.-R.); [EMAIL_ADDRESS] (A.G.-R.); [EMAIL_ADDRESS] (D.G.-L.); [EMAIL_ADDRESS] (I.G.) 1 Research Group Solar and Wind Feasibility Technologies (SWIFT),  
Electromechanical Engineering Department, Universidad de Burgos, Escuela Politecnica Superior,  
90000 Burgos, Spain: [EMAIL_ADDRESS] (S.G.-R.); [EMAIL_ADDRESS] (A.G.-R.); [EMAIL_ADDRESS] (D.G.-L.); j UVER is determined by applying a spectral weighting function known as the erythema spectral weighting function. This function quantifies the effectiveness of radiation at each wavelength \((\lambda)\) to cause minimal erythema. The UVER value is obtained by weighting the spectral irradiance of the UV radiation at each wavelength using the corresponding erythema effectiveness factor and then summing up these weighted values for all wavelengths present in the source spectrum, as specified in the standard. Due to the lack of UV and UVER sensors in many ground-based weather stations [15], these variables are often estimated from other radiometric or meteorological parameters. The effect of cloudiness on UVER has been analyzed for all-sky conditions. In overcast conditions and skies with low clouds, UVER decreases as cloudiness increases [16]. Cloud optical thickness and UVER radiation have an exponential dependence, with higher attenuation occurring in low clouds [17]. Therefore, the solar zenith angle (\(\theta_{z}\)) is one of the most influential parameters in the variation of UVER [18]. As \(\theta_{z}\) increases, there is a corresponding decrease in UVER [16; 19]. Notably, a reduction of up to 40% is observed when the zenith angle increases from 20\({}^{\circ}\) to 50\({}^{\circ}\)[19]. The relationship between UVER, relative optical air mass, and atmospheric clearness has been analyzed, concluding that atmosphere transmissivity to UVER exhibits higher sensitivity to changes in atmospheric clearness compared to variations in the total ozone column (TOC) [20]. Previous research has assessed the effect of ozone on UVER, revealing that higher ozone levels lead to a decrease in UVER due to the ozone absorption band within the UV range [18; 21]. The dependence of UVER on ozone is influenced by the variation of the zenith angle [13]. The influence of TOC on UVER is considerably smaller under overcast skies than under clear skies [22]. Different mathematical models have been developed to model UVER as a function of different meteorological variables. Empirical and radiative transfer models have been used to correlate UV radiation, solar broadband radiation, and atmospheric parameters (cloudiness, TOC, aerosols) [12; 18; 19; 23; 24; 25; 26]. Linear regressions (LR) have analyzed the effect of some geometric and atmospheric parameters on the ratio between global horizontal UV erytimal irradiance (GHUVE) and global horizontal irradiance (GHI). The aerosol load, TOC, and precipitable water exhibit a linear relationship with respect to GHUVE/GHI while \(\theta_{z}\) and clearness index, \(\mathrm{k_{t}}\), defined as the ratio of GHI over the corresponding extraterrestrial irradiance, exhibit exponential and polynomial behaviors, respectively [27]. Additionally, LR was employed to analyze the GHUVE/GHI ratio at various altitudes, revealing a strong correlation between these variables. The determination coefficient (R\({}^{2}\)) exhibits an increasing trend with higher altitudes [12]. In recent years, the use of machine learning (ML) algorithms for modeling climatic and meteorological data has become widespread [28]. These algorithms allow us to solve complex problems with higher performance than classical modeling [29].