Deep learning architecture for air quality predictions

By Xiang Li, Ling Peng, Yuan Hu, Jing Shao, Tianhe Chi. 2016

317 citations recorded by the source corpus.

Subfields: Automotive Engineering, Environmental Engineering, Health, Toxicology and Mutagenesis

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Ala Al-Fuqaha
2. Mohsen Guizani
3. Sameh Sorour
4. Huanfeng Shen
5. M. Mohammadi
6. Liangpei Zhang

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “[118] developed a DL-based air quality prediction model using a stacked AE for unsupervised feature extraction and a logistic regression model for regression of the final predictions.”
• Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach (2017, methodology): “This model is established and evaluated based on satellite observations, meteorological parameters, and ground-level PM 2.5 measurements from China, which is suffering from serious PM 2.5 pollution (Peng et al., 2016).”
• Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation. (2017, result): “Six different models, including our LSTME, the traditional LSTM NN, the STDL, the TDNN, the ARMA and the SVR models, were compared using the same dataset.”
• A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities (2018, background): “In 2016, Li et al. adopted the Stacked Autoencoder (SAE) architecture to forecast the PM 2.5 concentration of various regions [12].”