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.