Modeling Horizontal Ultraviolet Irradiance for All Sky Conditions by Using Artificial Neural Networks and Regression Models

Dieste-Velasco, M.I.; Rodríguez, Sol García; García-Rodríguez, Ana; Díez-Mediavilla, M.; Alonso-Tristán, Cristina

doi:10.3390/app13031473

Cited by 3 publications

(3 citation statements)

References 38 publications

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“…In this work, ANNs were used to estimate GHUVE through the Levenberg-Marquardt Back-Propagation (LMBP) algorithm. The ANN architecture adopted for this purpose consists of a single hidden layer and a single output, as outlined in a previous publication [28,29]. In the input layer, each neuron is a meteorological variable.…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

“…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]. Among the ML techniques, Artificial Neural Networks (ANN) are particularly noteworthy as they act as "black boxes" that establish mathematical relationships between the inputs and the output data without prior knowledge of the specific relationship (linear or nonlinear) existing between them [30].…”

Section: Introductionmentioning

confidence: 99%

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Ultraviolet Erythemal Irradiance (UVER) under Different Sky Conditions in Burgos, Spain: Multilinear Regression and Artificial Neural Network Models

García-Rodríguez,

Granados-López

et al. 2023

Applied Sciences

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Different strategies for modeling Global Horizontal UltraViolet Erythemal irradiance (GHUVE) based on meteorological parameters measured in Burgos (Spain) have been developed. The experimental campaign ran from September 2020 to June 2022. The selection of relevant variables for modeling was based on Pearson’s correlation coefficient. Multilinear Regression Model (MLR) and artificial neural network (ANN) techniques were employed to model GHUVE under different sky conditions (all skies, overcast, intermediate, and clear skies), classified according to the CIE standard on a 10 min basis. ANN models of GHUVE outperform those based on MLR according to the traditional statistical indices used in this study (R2, MBE, and nRMSE). Moreover, the work proposes a simple all-sky ANN model of GHUVE based on usually recorded variables at ground meteorological stations.

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Section: Artificial Neural Network Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ultraviolet Erythemal Irradiance (UVER) under Different Sky Conditions in Burgos, Spain: Multilinear Regression and Artificial Neural Network Models

García-Rodríguez,

Granados-López

et al. 2023

Applied Sciences

Self Cite

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“…El procedimiento anteriormente descrito se ha utilizado en el segundo de los artículos que conforman esta tesis [102], para modelar la irradiancia ultravioleta global sobre el plano horizontal en Burgos, a partir de los datos diezminutales experimentalmente obtenidos entre marzo de 2020 y mayo de 2022 en la instalación meteorológica.…”

Section: Modelización De La Irradiancia Ultravioleta Horizontal Para ...unclassified

Modelización matemática de la radiación solar ultravioleta

García Rodríguez

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Solar Ultraviolet Radiation (UVR), which is identified as a major environmental health hazard, is responsible for a variety of photochemical reactions with direct effects on urban and aquatic ecosystems, human health, plant growth, and the deterioration of industrial systems. Ground measurements of total solar UVR are scarce, with low spatial and temporal coverage around the world, which is mainly due to measurement equipment maintenance costs and the complexities of equipment calibration routines; however, models designed to estimate ultraviolet rays from global radiation measurements are frequently used alternatives. In an experimental campaign in Burgos, Spain, between September 2020 and June 2022, average values of the ratio between horizontal global ultraviolet irradiance (GHUV) and global horizontal irradiance (GHI) were determined, based on measurements at ten-minute intervals. Sky cloudiness was the most influential factor in the ratio, more so than any daily, monthly, or seasonal pattern. Both the CIE standard sky classification and the clearness index were used to characterize the cloudiness conditions of homogeneous skies. Overcast sky types presented the highest values of the ratio, whereas the clear sky categories presented the lowest and most dispersed values, regardless of the criteria used for sky classification. The main conclusion, for practical purposes, was that the ratio between GHUV and GHI can be used to model GHUV.

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A 10 km daily-level ultraviolet-radiation-predicting dataset based on machine learning models in China from 2005 to 2020

Jiang,

Shi,

et al. 2024

Earth Syst. Sci. Data

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Abstract. Ultraviolet (UV) radiation is closely related to health; however, limited measurements have hindered further investigation of its health effects in China. Machine learning algorithms have been widely used to predict environmental factors with high accuracy, but a limited number of studies have implemented it for UV radiation. The main aim of this study is to develop a UV radiation prediction model using the random forest approach and predict the UV radiation with a daily and 10 km resolution in mainland China from 2005 to 2020. The model was developed with multiple predictors, such as UV radiation data from satellites as independent variables and ground UV radiation measurements from monitoring stations as the dependent variable. Missing satellite-based UV radiation data were obtained using the 3 d moving average method. The model performance was evaluated using multiple cross-validation (CV) methods. The overall R2 and root mean square error between measured and predicted UV radiation from model development and model 10-fold CV were 0.97 and 15.64 W m−2 and 0.83 and 37.44 W m−2 at the daily level, respectively. The model that incorporated erythemal daily dose (EDD) retrieved from the Ozone Monitoring Instrument (OMI) had a higher prediction accuracy than that without it. Based on predictions of UV radiation at the daily level, 10 km spatial resolution, and nearly 100 % spatiotemporal coverage, we found that UV radiation increased by 4.20 %, PM2.5 levels decreased by 48.51 %, and O3 levels increased by 22.70 % from 2013–2020, suggesting a potential correlation among these environmental factors. The uneven spatial distribution of UV radiation was associated with factors such as latitude, elevation, meteorological factors, and season. The eastern areas of China pose a higher risk due to both high population density and high UV radiation intensity. Using a machine learning algorithm, this study generated a gridded UV radiation dataset with extensive spatiotemporal coverage, which can be utilized for future health-related research. This dataset is freely available at https://doi.org/10.5281/zenodo.10884591 (Jiang et al., 2024).

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Modeling Horizontal Ultraviolet Irradiance for All Sky Conditions by Using Artificial Neural Networks and Regression Models

Cited by 3 publications

References 38 publications

Ultraviolet Erythemal Irradiance (UVER) under Different Sky Conditions in Burgos, Spain: Multilinear Regression and Artificial Neural Network Models

Ultraviolet Erythemal Irradiance (UVER) under Different Sky Conditions in Burgos, Spain: Multilinear Regression and Artificial Neural Network Models

Modelización matemática de la radiación solar ultravioleta

A 10 km daily-level ultraviolet-radiation-predicting dataset based on machine learning models in China from 2005 to 2020

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