Prasad, Salvin S. and Deo, Ravinesh C. 
ORCID: https://orcid.org/0000-0002-2290-6749 and Downs, Nathan ORCID: https://orcid.org/0000-0002-3191-6404 and Igoe, Damien and Parisi, Alfio V. ORCID: https://orcid.org/0000-0001-8430-8907 and Soar, Jeffrey ORCID: https://orcid.org/0000-0002-4964-7556
(2022)
Cloud Affected Solar UV Predictions with Three-Phase Wavelet Hybrid Convolutional Long Short-Term Memory Network Multi-Step Forecast System.
IEEE Access.
pp. 1-18.
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Text (Published - ArticleFirst Version)
Cloud_Affected_Solar_UV_Predictions_with_Three-Phase_Wavelet_Hybrid_Convolutional_Long_Short-Term_Memory_Network_Multi-Step_Forecast_System.pdf Available under License Creative Commons Attribution 4.0. Download (1MB) | Preview |
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.
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| Item Type: | Article (Commonwealth Reporting Category C) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Faculty/School / Institute/Centre: | Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -) |
| Faculty/School / Institute/Centre: | Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -) |
| Date Deposited: | 01 Mar 2022 05:17 |
| Last Modified: | 01 Mar 2022 05:17 |
| Uncontrolled Keywords: | Clouds; Predictive models; Cancer Prediction; algorithms; Indexes; Forecasting; Feature extraction |
| Fields of Research (2020): | 37 EARTH SCIENCES > 3701 Atmospheric sciences > 370106 Atmospheric radiation 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation |
| Socio-Economic Objectives (2020): | 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences |
| Identification Number or DOI: | https://doi.org/10.1109/ACCESS.2022.3153475 |
| URI: | http://eprints.usq.edu.au/id/eprint/47315 |
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