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

TL;DR

This paper employs advanced LSTM deep learning models to predict short-term and long-term air quality in Delhi, accounting for COVID-19 lockdown effects, and demonstrates the bidirectional-LSTM's superior predictive performance.

Contribution

It introduces multivariate bidirectional-LSTM models for air quality prediction, incorporating COVID-19 lockdown impacts and quantifying uncertainties in long-term forecasts.

Findings

Bidirectional-LSTM outperforms other models in accuracy.

COVID-19 lockdown significantly affected air quality.

Post-lockdown air quality worsened unexpectedly.

Abstract

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.