PmForecast: leveraging temporal LSTM to deliver in situ air quality predictions-Reference-Cited by-同舟云学术

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PmForecast: leveraging temporal LSTM to deliver in situ air quality predictions

Published:2024-08-10 Issue:39 Volume:31 Page:51760-51773
ISSN:1614-7499
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Rahmani Maryam,Crumeyrolle Suzanne,Allegri-Martiny Nadége,Taherkordi Amir,Rouvoy Romain

Funder

H2020 Marie Skłodowska-Curie Actions

The Research Council of Norway

Ministry of Ecological Transition and Territorial Cohesion

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11356-024-34623-w.pdf

Reference46 articles.

1. Abimannan S, Chang YS, Lin CY (2020) Air pollution forecasting using LSTM-multivariate regression model. In: Hsu CH, Kallel S, Lan KC, et al (eds) Internet of Vehicles. Technologies and Services Toward Smart Cities. Springer International Publishing, Cham, pp 318–326, https://doi.org/10.1007/978-3-030-38651-1_25

2. Alhirmizy S, Qader B (2019) Multivariate time series forecasting with LSTM for Madrid, Spain pollution. In: 2019 International Conference on Computing and Information Science and Technology and Their Applications (ICCISTA), pp 1–5, https://doi.org/10.1109/ICCISTA.2019.8830667

3. Annesi-Maesano I, Forastiere F, Kunzli N et al (2007) Particulate matter, science and EU policy. Eur Respir Soc 29(3):428–431. https://doi.org/10.1183/09031936.00129506

4. Bui T, Le V, Cha S (2018) A deep learning approach for air pollution forecasting in South Korea LSTM. CoRR abs/1804.07891.https://doi.org/10.48550/arXiv.1804.07891,

5. Che Lah MS, Arbaiy N, Lin PC (2023) ARIMA-LP: a hybrid model for air pollution forecasting with uncertainty data. In: 2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS), pp 353–356, https://doi.org/10.1109/ICSECS58457.2023.10256363

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