Multi-step solar UV index prediction using deep learning methods

Abstract

The impact of ultraviolet (UV) radiation on public health is severe and can cause sunburn, skin aging and cancer, immunosuppression, and eye damage. Minimization of exposure to solar UV is required in order to reduce the risks of these illnesses to the public. Greater public awareness and a prediction of the ultraviolet index (UVI) is considered an essential task for the minimization of solar UV exposures. This research has designed artificial intelligence based deep learning models to predict multistep UVI index. It has developed a convolutional neural network integrated with long short-term memory network (CLSTM) as the main model to forecast UVI for Brisbane with latitude − 27.47 and longitude 153.02, the capital city of Queensland, Australia. Solar zenith angle (SZA) data were used together with UVI as inputs in the CLSTM for 10-min, 30-min and 60-min UVI prediction. The CLSTM model was benchmarked against long short-term memory network (LSTM), convolutional neural network (CNN), Deep Neural Network (DNN), multilayer perceptron (MLP), extreme learning machine (ELM), random forest regression (RFR), Extreme Gradient Boosting (XGB), and Pro6UV Deterministic models. The experimental results showed that the CLSTM model outperformed these models with RMSE = 0.3817, MAE = 0.1887, RRMSE = 8.0086%, MAPE = 4.6172% and APB = 3.9586 for 10-min prediction. In addition to that, these metrics for 30-min and 60-min prediction were RMSE = 0.4866/0.5146, MAE = 0.2763/0.3038, RRMSE = 10.4860%/11.5840%, MAPE = 8.1037%/9.6558% and APB = 5.9546/6.8386, respectively. Thus, the CLSTM model can yield improved UVI prediction for both the public and the government agencies.

Analysis of the ultraviolet index in the region of Puno, Peru

“…Se ha desarrollado un modelo de aprendizaje profundo basado en inteligencia para predecir el índice UV. El modelo sistema nervioso convolucional red integrada con red de memoria a largo plazo (CLSTM) superó a otros modelos y puede producir predicciones mejoradas para el público y agencias gubernamentales [34].…”

Cargador De Móvil Para Bicicletas

“…La amalgama disruptiva de una arquitectura de red neuronal convolucional en conjunción con una red de memoria de largo plazo (CLSTM) prevaleció sobre alternativas preexistentes, evidenciando capacidad para producir proyecciones perfeccionadas tanto para el público en general como para entidades gubernamentales. (Al-musaylh et al, 2023).…”