## 1 Introduction
Other models were used to estimate I\({}_{\rm UV}\) under various sky conditions, analyzing the dependence of UV irradiance on the brightness index, \(\Delta\), and the solar elevation angle [27]. It has been shown, using the brightness index as a parameter to model cloudiness, that clouds hardly reduce the UV component, but the horizontal global component does [3]. If a specific brightness is used, it is observed that the UV component increases almost exponentially with the zenith angle [29]. In addition, different statistical models have been developed to estimate daily I\({}_{\rm UV}\) from I\({}_{\rm GH}\) through the linear correlation; these introduce a polynomial correction of the average ratio I\({}_{\rm UV}\)/I\({}_{\rm GH}\) as a function of the transmittance index of global solar irradiance and the UV atmospheric transmittance index by means of a multiple regression of the air mass and clearness index [11]. Other researchers have obtained semi-empirical models based on the SMARTS radiative transfer model [30], using atmospheric precipitable water, total ozone column, aerosol optical depth, daily temperature, relative humidity, atmospheric pressure, and dew point temperature as the input variables [13]. In this case, the I\({}_{\rm UV}\) as modeled is considered to be the product of the cloud modification factor of UV irradiance multiplied by the simulated UV irradiance under clear sky conditions. For a quantitative evaluation of the effect of clouds and aerosols on radiation, the so-called daily attenuation coefficients for cloud and aerosols were defined. In recent years, the use of ANNs is becoming frequent for the calculation of UV component values because they possess some advantages over classic modeling. They do not require a user-specified problem-solving algorithm, and they can respond to patterns which are similar, but not identical, to that for which they were trained [31]. However, the ANN is often a \"black box\" system; its use is not a user-friendly task for a non-expert [20]. In all the works found in the literature in the field of ANN, I\({}_{\rm GH}\) has been used as input and its use is now the norm to obtain accurate results when modelling I\({}_{\rm UV}\), regardless of the number and the characteristics of the other variables used as inputs and the architecture of the ANN [20; 29; 32; 33]. In addition, other authors have developed UV estimation models through an empirical artificial neural network (ANN) and support vector machine (SVM), using different variables for each method in order to compare the results obtained for the modeled values of the UV component [34]. Moreover, other magnitudes related to UV radiation have also been modeled using ANNs, such as the ratio I\({}_{\rm UV}\)/I\({}_{\rm GH}\), considering the transmittance index of I\({}_{\rm GH}\) and the atmospheric transmittance index as the inputs, or the UV atmospheric transmittance index,using the air mass and the clearness index as the inputs [11]. Statistical models and ANNs showed good statistical performance with an RMS lower than 5% and an MBE between 0.4 and 2% [11]. All models can be used to estimate UV radiation at locations where only I\({}_{\text{GH}}\) data are available [11]. ANN models have been developed to estimate solar UV erytmal irradiance (UVER), based on a combination of optical air mass, ozone column content, and I\({}_{\text{GH}}\). The input data were collected at different locations, obtaining a mean bias deviation of less than 1% and a root mean square deviation of less than 17% for all locations [35]. In this work, different models are proposed for the determination of the I\({}_{\text{UV}}\) based on meteorological variables, using data collected at Burgos (Spain). To do so, supervised artificial neural networks (ANNs) are used and a comparison is also made with the results obtained through regression models.