Abstract
Air pollution, a pressing issue in Vietnam, is primarily caused by elevated levels of particulate matter 2.5 (PM2.5). Accurate PM2.5 forecasting is crucial for effective air quality management. This study uses advanced approaches, such as Long Short Term Memory (LSTM-based) models, to estimate PM2.5 levels in Vietnamese locations. Ten locations were initially chosen for prediction, but the pre-processing reduced the number to six due to missing critical values. The study constructs and rigorously tests prediction models using historical meteorological data with conventional statistical indicators. It also explores model combinations for increased reliability and acknowledges persistent problems in improving accuracy and real-world application. In conclusion, this work provides unique insights into PM2.5 forecasting, particularly the superior performance of the LSTM-based model, to aid in mitigating the causes of air pollution.
Q.-D. Nguyen and T. A. H. Nguyen—The first two authors contributed equally to this work.
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Acknowledgments
The authors extended their gratitude to PAM Air for sponsoring the study and providing access to a complimentary air quality monitoring network, thereby enhancing the scope of this research. Dr. Tran Thanh Tu, a lecturer at the School of Biotechnology, International University, contributed valuable expertise and guidance, facilitating the successful execution of scientific conduct. This research is also supported by The central Interdisciplinary Laboratory in Electronics and Information Technology “AI and Cooperation Robot,” International University - Vietnam National University of Ho Chi Minh City.
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Nguyen, QD. et al. (2024). Investigation of Machine Learning and Deep Learning Approaches for Early PM2.5 Forecasting: A Case Study in Vietnam. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2024. Lecture Notes in Computer Science(), vol 14810. Springer, Cham. https://doi.org/10.1007/978-3-031-70816-9_24
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