Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdown
This work addresses air quality forecasting for public health and environmental management in Delhi, but it is incremental as it applies existing LSTM variants to new data.
The paper tackled short-term and long-term air quality prediction in Delhi using LSTM models, showing that a multivariate bidirectional-LSTM model provided the best predictions across COVID-19 lockdown periods, with forecasts covering up to 80 hours and one month ahead.
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.