Cloud Affected Solar UV Prediction With Three-Phase Wavelet Hybrid Convolutional Long Short-Term Memory Network Multi-Step Forecast System

Harmful exposure to erythemally-effective ultraviolet radiation (UVR) poses high health risks such as malignant keratinocyte cancers and eye-related diseases. Delivering short-term forecasts of the solar ultraviolet index (UVI) is an effective way to advise UVR exposure information to the public at risk. This research reports on a novel framework built to forecast UVI, integrating antecedent lagged memory of cloud statistical properties and the solar zenith angle (SZA). To produce the forecasts at multi-step horizon we design a 3-phase hybrid convolutional long short-term memory network (W-O-convLSTM) model, validated with Queensland-based datasets in near real-time (
i.e
., 10-minute, 20-minute, 30-minute and 1 hour forecast horizon). Our approach in optimizing the performance also entails a robust selective filtering method using the BorutaShap algorithm, data decomposition with stationary wavelet transformation and hyperparameter optimization using the Optuna algorithm. We assess the performance of the proposed W-O-convLSTM model alongside the baseline and benchmark models. The captured results, through statistical metrics and visual infographics, elucidate the superior performance of the objective model in short-term UVI forecasting. For instance, at a 10-minute forecast horizon, our objective model yields a relatively high correlation coefficient of ~0.961 in the autumn, 0.909 in the summer, 0.926 in the spring and 0.936 in the winter season. Overall, the proposed O-convLSTM model outperforms its competing counterpart models for all forecast horizons with the lowest absolute forecast error. The robustness of our newly proposed model avers its practical utility in delivering sun-protection behavior recommendations that can mitigate UV-exposure-related public health risk. We also recommend that future integration of aerosol and ozone effects with cloud cover data can enhance our forecasting framework for wider applications in solar energy or skin health monitoring systems.