Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation.

By Xiang Li, Ling Peng, Xiaojing Yao, Shaolong Cui, Yuan Hu, Chengzeng You, Tianhe Chi. Environmental Pollution, 2017

578 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/45b9424e1b719d05c1ea08d3a1105e8d32ba94e7

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. Rahim Barzegar
2. Jan Adamowski
3. Mohammad Taghi Aalami
4. Ravinesh C. Deo
5. Mukesh Tiwari
6. Muhammad Mohsin Waqas
7. Usa Wannasingha Humphries
8. John M. Quilty

Citations in the corpus

• A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities (2018, methodology): “showed that the air pollution estimation system based on a Long Short-Term Memory (LSTM) neural network is more accurate [13].”
• Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model (2020, background): “However, as indicated by Li et al. (2017), it is more difficult to make a long-term prediction because long-term prediction tasks require more pertinent historical input data than a short-term prediction.”