Deep Learning-Based Forecasting of Ultraviolet Radiation Intensity in Lima, Peru: Implications for Climate Resilience and Public Health

Ultraviolet (UV) radiation is a major environmental risk associated with skin cancer, premature skin aging, and ocular damage. In the context of climate variability, changes in cloud cover and ozone-layer dynamics increase the need for reliable short-term UV forecasting systems in highly exposed urban areas. This study proposes a comparative forecasting framework for UV radiation intensity in Lima, Peru, using more than 827,000 records from a meteorological station. Statistical models, recurrent deep learning architectures, and hybrid neural models were evaluated under a unified protocol including 5 min aggregation, daytime filtering, a fixed 60 min forecasting horizon, chronological train–test partitioning, temporal cross-validation, statistical significance testing, and quantitative residual diagnostics. The results show that recurrent and hybrid deep learning models substantially outperformed traditional statistical approaches. Hybrid Model 2 achieved the best holdout performance, obtaining the lowest RMSE and the highest R2 value. Statistical testing confirmed its superiority over classical forecasting models. Residual diagnostics showed limited systematic bias, although extreme UV radiation peaks remained the principal source of forecasting uncertainty. These findings provide a reproducible artificial intelligence framework for short-term UV radiation forecasting and support intelligent early warning systems for public health protection, environmental monitoring, and climate resilience, contributing to Sustainable Development Goal 13 on Climate Action.