The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Deep learning architecture for air quality predictions
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Citations
Cites
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
Spatial analysis of air pollution and mortality in Los Angeles
Jerrett, M., Burnett, R. T., Ma, R., Pope, C. A., Krewski, D., Newbold, K. B., Thurston, G., Shi, Y., Finkelstein, N., Calle, E. E., et al. (2005) · Epidemiology · doi:10.1097/01.ede.0000181630.15826.7d · Cited by 985
Cited by
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2844341 · Cited by 1,471
A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities
Huang, C.-J., & Kuo, P.-H. (2018) · Sensors · doi:10.3390/s18072220 · Cited by 705
A review of artificial neural network models for ambient air pollution prediction
Cabaneros, S. M., Calautit, J. K., & Hughes, B. R. (2019) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2019.06.014 · Cited by 459
Li, T., Shen, H., Yuan, Q., Zhang, X., & Zhang, L. (2017) · Geophysical Research Letters · doi:10.1002/2017gl075710 · Cited by 414
Popescu, S. M., Mansoor, S., Wani, O. A., Kumar, S. S., Sharma, V., Sharma, A., Arya, V. M., Kirkham, M. B., Hou, D., Bolan, N., et al. (2024) · Frontiers in Environmental Science · doi:10.3389/fenvs.2024.1336088 · Cited by 334
Metadata
- Authors
- Li, X., Peng, L., Hu, Y., Shao, J., & Chi, T.
- Venue
- Environmental Science and Pollution Research
- Published
- 2016-10-13
- Volume
- 23
- Issue
- 22
- Pages
- 22408-22417
- ISSN
- 0944-1344 (ISSN-L)
- 1614-7499
- OpenAlex
- W2530443992
- Cited by
- 343
- References
- 6
- FWCI
- 14.32
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Deep learning architecture for air quality predictions. https://app.cochat.ai/papers/10.1007/s11356-016-7812-9
- The paper
- Li, X., Peng, L., Hu, Y., Shao, J., & Chi, T. (2016). Deep learning architecture for air quality predictions. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-016-7812-9
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
