Deep Learning for PM2.5 Forecasting: An Empirical Comparison of LSTM, GRU, and N-BEATS
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DOI: https://doi.org/10.2478/rtuect-2026-0036 | Journal eISSN: 2255-8837 (formerly 2255-8845) | Journal ISSN: 1691-5208
Language: English
Page range: 526 - 541
Submitted on: Mar 16, 2026
Accepted on: Apr 20, 2026
Published online: Oct 23, 2026
Published in print: Jan 1, 2026
Published by: Riga Technical University
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open
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© 2026 Murad A. Yaghi, Huthaifa Al-Omari, Salem Alemaishat, Raghad Abu Wadi, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.