A Comparative Study of Features Selection in the Context of Forecasting PM2.5 Concentration
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-4764-5_16
Reference12 articles.
1. US EPA (2021) Particulate Matter (PM) Basics _ US EPA. In: Particulate Matter Pollution. https://www.epa.gov/pm-pollution/particulate-matter-pm-basics. Accessed 27 Nov 2022
2. Huang G, Li X, Zhang B, Ren J (2021) PM2.5 concentration forecasting at surface monitoring sites using GRU neural network based on empirical mode decomposition. Sci Total Environ 768:144516. https://doi.org/10.1016/j.scitotenv.2020.144516
3. Du S, Li T, Yang Y, Horng SJ (2021) Deep air quality forecasting using hybrid deep learning framework. IEEE Trans Knowl Data Eng 33:2412–2424. https://doi.org/10.1109/TKDE.2019.2954510
4. Freeman BS, Taylor G, Gharabaghi B, Thé J (2018) Forecasting air quality time series using deep learning. J Air Waste Manage Assoc 68:866–886. https://doi.org/10.1080/10962247.2018.1459956
5. da Silva DG, Geller MTB, Santos Moura dos MS, de Mauro Meneses AA (2022) Performance evaluation of LSTM neural networks for consumption prediction. e-Prime Adv Electr Eng, Electr Energy 2:100030. https://doi.org/10.1016/J.PRIME.2022.100030
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