Cloud Affected Solar UV Predictions with Three-Phase Wavelet Hybrid Convolutional Long Short-Term Memory Network Multi-Step Forecast System
Article
Article Title | Cloud Affected Solar UV Predictions with Three-Phase Wavelet Hybrid Convolutional Long Short-Term Memory Network Multi-Step Forecast System |
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ERA Journal ID | 210567 |
Article Category | Article |
Authors | Prasad, Salvin S. (Author), Deo, Ravinesh C. (Author), Downs, Nathan (Author), Igoe, Damien (Author), Parisi, Alfio V. (Author) and Soar, Jeffrey (Author) |
Journal Title | IEEE Access |
Journal Citation | 10, pp. 24704-24720 |
Number of Pages | 17 |
Year | 2022 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 2169-3536 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2022.3153475 |
Web Address (URL) | https://ieeexplore.ieee.org/document/9718335 |
Abstract | 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. Our approach in optimizing the performance 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 accurate sun-protection behavior recommendations to mitigate UV-exposure-related public health risk. In accordance with our findings, we recommend that future integration of aerosol and ozone effects with cloud cover data may further enhance our UVI forecasting framework. |
Keywords | Clouds; Predictive models; Cancer Prediction; algorithms; Indexes; Forecasting; Feature extraction |
Related Output | |
Is part of | Solar ultraviolet radiation predictions under cloud cover effects with artificial intelligence approaches |
ANZSRC Field of Research 2020 | 370106. Atmospheric radiation |
460207. Modelling and simulation | |
Public Notes | This article is part of a UniSQ Thesis by publication. See Related Output. |
Byline Affiliations | School of Mathematics, Physics and Computing |
School of Business | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q71z8/cloud-affected-solar-uv-predictions-with-three-phase-wavelet-hybrid-convolutional-long-short-term-memory-network-multi-step-forecast-system
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License: CC BY 4.0 | ||
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