Enhanced joint hybrid deep neural network explainable artificial intelligence model for 1-hr ahead solar ultraviolet index prediction

• Published in: Computer methods and programs in biomedicine 2023-11, Vol.241, p.107737-107737, Article 107737
• Main Authors: Prasad, Salvin S., Deo, Ravinesh C., Salcedo-Sanz, Sancho, Downs, Nathan J., Casillas-Pérez, David, Parisi, Alfio V.
• Format: Article
• Language: English
• Subjects: Bayesian optimization; Black-box models; Deep neural networks; Explainable artificial intelligence (xAI); Model-agnostic; Ultraviolet index
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2023.107737

Exposure to solar ultraviolet (UV) radiation can cause malignant keratinocyte cancer and eye disease. Developing a user-friendly, portable, real-time solar UV alert system especially or wearable electronic mobile devices can help reduce the exposure to UV as a key measure for personal and occupational management of the UV risks. This research aims to design artificial intelligence-inspired early warning tool tailored for short-term forecasting of UV index (UVI) integrating satellite-derived and ground-based predictors for Australian hotspots receiving high UV exposures. The study further improves the trustworthiness of the newly designed tool using an explainable artificial intelligence approach. An enhanced joint hybrid explainable deep neural network model (called EJH-X-DNN) is constructed involving two phases of feature selection and hyperparameter tuning using Bayesian optimization. A comprehensive assessment of EJH-X- DNN is conducted with six other competing benchmarked models. The proposed model is explained locally and globally using robust model-agnostic explainable artificial intelligence frameworks such as Local Interpretable Model-Agnostic Explanations (LIME), Shapley additive explanations (SHAP), and permutation feature importance (PFI). The newly proposed model outperformed all benchmarked models for forecasting hourly horizons UVI, with correlation coefficients of 0.900, 0.960, 0.897, and 0.913, respectively, for Darwin, Alice Springs, Townsville, and Emerald hotspots. According to the combined local and global explainable model outcomes, the site-based results indicate that antecedent lagged memory of UVI and solar zenith angle are influential features. Predictions made by EJH-X-DNN model are strongly influenced by factors such as ozone effect, cloud conditions, and precipitation. With its superiority and skillful interpretation, the UVI prediction system reaffirms its benefits for providing real-time UV alerts to mitigate risks of skin and eye health complications, reducing healthcare costs and contributing to outdoor exposure policy. •Explainable artificial intelligence xAI for 1-hr ahead solar ultraviolet UV index prediction.•xAI model is optimized with the Bayesian feature selection algorithm.•xAI methods provide physical understandings of model prediction outcomes.•xAI model is significantly accurate for solar UV index predictions.•xAI model can help in sun exposure risk mitigation, skin and eye health protection.