UVR Prediction Over Lagos, Ibadan and New Richmond for Public Health and Renewable Energy

Ultraviolet radiation (UVR) is a significant environmental factor influencing various biological and chemical processes, including photosynthesis in plants, vitamin D synthesis in humans, and microbial sterilization. However, excessive UVR exposure can lead to adverse effects such as skin cancer and DNA damage. This study applies the ARIMA (AutoRegressive Integrated Moving Average) model to predict UVR levels over Lagos and Ibadan in Nigeria, and New Richmond in the United States, utilizing a 21-year dataset spanning from 2000 to 2020. By analysing the autocorrelation function (ACF) and partial autocorrelation function (PACF) with a significance level of 0.25, the stationarity and appropriate parameters for the ARIMA model were identified. The model is then used to predict daily UVR values for the last day of each month from January 2021 to December 2022. Results indicate that the ARIMA model effectively captures the temporal patterns in UVR data, with validation metrics such as RMSE, MAE, and MAPE confirming its predictive accuracy. This predictive capability can inform public health advisories, agricultural practices, and environmental management, emphasizing the need for ongoing monitoring and prediction of UVR levels to mitigate potential health risks.