# Deep-AIR: A Hybrid CNN-LSTM Framework forFine-Grained Air Pollution Forecast

arXiv: https://arxiv.org/abs/2001.11957 · HTML: https://mlsanity.com/paper/2001.11957/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods](https://mlsanity.com/paper/2104.15007/) (2021)
- [Deep-AIR: A Hybrid CNN-LSTM Framework for Air Quality Modeling in Metropolitan Cities](https://mlsanity.com/paper/2103.14587/) (2021)
- [Long-lead forecasts of wintertime air stagnation index in southern China using oceanic memory effects](https://mlsanity.com/paper/2305.11901/) (2023)
- [Frequency-Aware Attention-LSTM for PM2.5 Time Series Forecasting](https://mlsanity.com/paper/2503.24043/) (2025)

## Abstract (arXiv)

Poor air quality has become an increasingly critical challenge for many metropolitan cities, which carries many catastrophicphysical and mental consequences on human health and quality of life. However, accurately monitoring and forecasting air qualityremains a highly challenging endeavour. Limited by geographically sparse data, traditional statistical models and newly emergingdata-driven methods of air quality forecasting mainly focused on the temporal correlation between the historical temporal datasets of airpollutants. However, in reality, both distribution and dispersion of air pollutants are highly location-dependant. In this paper, we proposea novel hybrid deep learning model that combines Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM)together to forecast air quality at high-resolution. Our model can utilize the spatial correlation characteristic of our air pollutant datasetsto achieve higher forecasting accuracy than existing deep learning models of air pollution forecast.