An ensemble NLSTM-based model for PM2.5 concentrations prediction considering feature extraction and data decomposition
Author:
Publisher
Springer Science and Business Media LLC
Subject
Health, Toxicology and Mutagenesis,Management, Monitoring, Policy and Law,Atmospheric Science,Pollution
Link
https://link.springer.com/content/pdf/10.1007/s11869-023-01385-2.pdf
Reference68 articles.
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2. Biancofiore F, Busilacchio M, Verdecchia M et al (2017) Recursive neural network model for analysis and forecast of PM10 and PM2.5. Atmos Pollut Res 8(4):652–659. https://doi.org/10.1016/j.apr.2016.12.014
3. Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
4. Breiman L (2017) Classification and regression trees. Routledge, England
5. Calderón-Garcidueñas L, Solt AC, Henríquez-Roldán C et al (2008) Long-term air pollution exposure is associated with neuroinflammation, an altered innate immune response, disruption of the blood-brain barrier, ultrafine particulate deposition, and accumulation of amyloid β-42 and α-synuclein in children and young adults. Toxicol Pathol 36(2):289–310. https://doi.org/10.1177/0192623307313011
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1. A data-driven approach for PM2.5 estimation in a metropolis: random forest modeling based on ERA5 reanalysis data;Environmental Research Communications;2024-03-01
2. The application of strategy based on LSTM for the short-term prediction of PM2.5 in city;Science of The Total Environment;2024-01
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