The solar ultraviolet index (UVI) is a key public health indicator to mitigate the ultraviolet-exposure related diseases. This study aimed to develop and compare the performances of different hybridised deep learning approaches with a convolutional neural network and long short-term memory referred to as CLSTM to forecast the daily UVI of Perth station, Western Australia. A complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is incorporated coupled with four feature selection algorithms (i.e., genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and differential evolution (DEV)) to understand the diverse combinations of the predictor variables acquired from three distinct datasets (i.e., satellite data, ground-based SILO data, and synoptic mode climate indices). The CEEMDAN-CLSTM model coupled with GA appeared to be an accurate forecasting system in capturing the UVI. Compared to the counterpart benchmark models, the results demonstrated the excellent forecasting capability (i.e., low error and high efficiency) of the recommended hybrid CEEMDAN-CLSTM model in apprehending the complex and non-linear relationships between predictor variables and the daily UVI. The study inference can considerably enhance real-time exposure advice for the public and help mitigate the potential for solar UV-exposure-related diseases such as melanoma.

The solar ultraviolet index (UVI) is a key public health indicator to mitigate the ultraviolet-exposure related diseases. However, in practice, the ultraviolet irradiance measurements are difficult and need expensive ground-based physical models and time-consuming satellite-observed data. Furthermore, accurate short-term forecasting is crucial for making effective decisions on public health owing to UVI related diseases. To this end, this study aimed to develop and compare the performances of different hybridized deep learning models for forecasting the daily UVI index. The ultraviolet irradiance-related data were collected for Perth station of Western Australia. A hybrid-deep learning framework was formulated with a convolutional neural network and long short-term memory called CLSTM. The comprehensive dataset (i.e., satellite-derived Moderate Resolution Imaging Spectroradiometer, ground-based datasets from Scientific Information for Landowners, and synoptic-scale climate indices) were fed into the proposed network and optimized by four optimization techniques. The results demonstrated the excellent forecasting capability (i.e., low error and high efficiency) of the recommended hybrid CLSTM model compared to the counterpart benchmark models. Overall, this study showed that the proposed hybrid CLSTM model successfully apprehends the complex and non-linear relationships between predictor variables and the daily UVI. A complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-CLSTM-based is appeared to be an accurate forecasting system capable of reacting quickly to measured conditions. Further, the genetic algorithm is found to be the most effective optimization technique. The study inference can considerably enhance real-time exposure advice for the public and help mitigate the potential for solar UV-exposure-related diseases such as melanoma.