Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model

Article


Ahmed, A. A. Masrur, Ahmed, Mohammad Hafez, Saha, Sanjoy Kanti, Ahmed, Oli and Sutradhar, Ambica. 2022. "Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model." Stochastic Environmental Research and Risk Assessment. 36 (10), pp. 3011-3039. https://doi.org/10.1007/s00477-022-02177-3
Article Title

Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model

ERA Journal ID864
Article CategoryArticle
AuthorsAhmed, A. A. Masrur, Ahmed, Mohammad Hafez, Saha, Sanjoy Kanti, Ahmed, Oli and Sutradhar, Ambica
Journal TitleStochastic Environmental Research and Risk Assessment
Journal Citation36 (10), pp. 3011-3039
Number of Pages29
Year2022
PublisherSpringer
Place of PublicationGermany
ISSN1436-3240
1436-3259
Digital Object Identifier (DOI)https://doi.org/10.1007/s00477-022-02177-3
Web Address (URL)https://link.springer.com/article/10.1007/s00477-022-02177-3
Abstract

The solar ultraviolet index (UVI) is a key public health indicator to mitigate the ultraviolet-exposure related diseases. This study aimed to develop and compare the performances of different hybridised deep learning approaches with a convolutional neural network and long short-term memory referred to as CLSTM to forecast the daily UVI of Perth station, Western Australia. A complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is incorporated coupled with four feature selection algorithms (i.e., genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and differential evolution (DEV)) to understand the diverse combinations of the predictor variables acquired from three distinct datasets (i.e., satellite data, ground-based SILO data, and synoptic mode climate indices). The CEEMDAN-CLSTM model coupled with GA appeared to be an accurate forecasting system in capturing the UVI. Compared to the counterpart benchmark models, the results demonstrated the excellent forecasting capability (i.e., low error and high efficiency) of the recommended hybrid CEEMDAN-CLSTM model in apprehending the complex and non-linear relationships between predictor variables and the daily UVI. The study inference can considerably enhance real-time exposure advice for the public and help mitigate the potential for solar UV-exposure-related diseases such as melanoma.

KeywordsDeep learning; Hybrid model; Solar ultraviolet index; Optimization algorithm; Public health
Public Notes

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Byline AffiliationsSchool of Mathematics, Physics and Computing
West Virginia University, United States
Norwegian University of Science and Technology, Norway
Leading University, Bangladesh
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