# Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdown

arXiv: https://arxiv.org/abs/2102.10551 · HTML: https://mlsanity.com/paper/2102.10551/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 121 references that are in the map.
- First posted on arXiv 21 Feb 2021.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Sequence-to-Sequence Neural Net Models for Grapheme-to-Phoneme Conversion](https://mlsanity.com/paper/1506.00196/) (2015)
- [Langevin-gradient parallel tempering for Bayesian neural learning](https://mlsanity.com/paper/1811.04343/) (2018)
- [Deep learning via LSTM models for COVID-19 infection forecasting in India](https://mlsanity.com/paper/2101.11881/) (2021)
- [An Experimental Study of LSTM Encoder-Decoder Model for Text Simplification](https://mlsanity.com/paper/1609.03663/) (2016)
- [A Deep Learning Approach for Forecasting Air Pollution in South Korea Using LSTM](https://mlsanity.com/paper/1804.07891/) (2018)
- [Densely Connected Bidirectional LSTM with Applications to Sentence Classification](https://mlsanity.com/paper/1802.00889/) (2018)

## Built on by (most cited first, 1 of 1)

- [COVID-19 sentiment analysis via deep learning during the rise of novel cases](https://mlsanity.com/paper/2104.10662/) (2021)

## Abstract (arXiv)

Air pollution has a wide range of implications on agriculture, economy, road accidents, and health. In this paper, we use novel deep learning methods for short-term (multi-step-ahead) air-quality prediction in selected parts of Delhi, India. Our deep learning methods comprise of long short-term memory (LSTM) network models which also include some recent versions such as bidirectional-LSTM and encoder-decoder LSTM models. We use a multivariate time series approach that attempts to predict air quality for 10 prediction horizons covering total of 80 hours and provide a long-term (one month ahead) forecast with uncertainties quantified. Our results show that the multivariate bidirectional-LSTM model provides best predictions despite COVID-19 impact on the air-quality during full and partial lockdown periods. The effect of COVID-19 on the air quality has been significant during full lockdown; however, there was unprecedented growth of poor air quality afterwards.