Hybrid attention based deep learning for forecasting boundary layer ozone using satellite derived profiles

Band S.S. Qasem S.N. Ramezani J. Abbaspour M. Pai H.-T. Gupta B.B. Mansor Z. Mosavi A.
1 January 2026 Academic Press

Ecotoxicology and Environmental Safety
2026 #309

Ground level ozone is considered a major air pollutant. It is formed when nitrogen oxides and volatile organic compounds react under sunlight. It harms human health and damages plants and materials. It also contributes to climate change. It is a photochemically formed compound that is extremely hazardous to the environment and human health. Proper forecasting of the boundary layer ozone has been a challenge due to the nonlinearly related to both meteorological and chemical conditions and the scarcity of fine-scale vertical ozone patterns. This study uses the OMPROFOZ ozone profile product which is a product of the Ozone Monitoring Instrument (OMI) on the Aura satellite to estimate the ozone concentrations in the boundary layer. A set of deep learning models, i.e., RNN, CNN, GRU, LSTM, and hybrid forms i.e., GRU-CNN and LSTM-CNN, is evaluated to benchmark forecasting accuracy. The first, ConvBiGRU-AttentionNet, integrates attention mechanisms within a convolutional gated recurrent structure. The second, EMD-ConvBiGRU-AttentionNet, adds Empirical Mode Decomposition to extract multi-scale temporal features before modeling. The proposed models outperform conventional methods across metrics such as RMSE, MAE, ^R2, and skill scores. EMD-ConvBiGRU-AttentionNet achieves the highest prediction accuracy. Visual analyses, i.e., residual plots, cumulative error distributions, and attention maps, confirm the capacity of the model to capture spatio-temporal patterns in atmospheric data.

Artificial intelligence , Attention mechanism , Big data , Boundary layer ozone , Data Science , Deep learning , Empirical Mode Decomposition , Machine learning , Remote sensing

Text of the article Перейти на текст статьи

Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan
Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
Department of Information Management, National Yunlin University of Science and Technology, Yunlin, Taiwan
Department of Big Data Business Analytics, National Pingtung University, Pingtung, Taiwan
Department of Computer Science and Information Engineering, Asia University, Taichung, 413, Taiwan
Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India
Research Center for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor Darul Ehsan, Bandar Baru Bangi, 43600, Malaysia
John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
University of Public Service, Budapest, Hungary
Univerzita J. Selyeho Komarom, Slovakia
Abylkas Saginov Karaganda Technical University, Karaganda, Kazakhstan
School of Cybersecurity, Korea University, Seoul, South Korea
Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, South Korea

Department of Information Management
Computer Science Department
Department of Information Management
Department of Big Data Business Analytics
Department of Computer Science and Information Engineering
Department of Medical Research
Symbiosis Centre for Information Technology (SCIT)
Research Center for Software Technology and Management
John von Neumann Faculty of Informatics
University of Public Service
Univerzita J. Selyeho Komarom
Abylkas Saginov Karaganda Technical University
School of Cybersecurity
Kyung Hee University

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026