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

2021-02-21 · Animesh Tiwari, Rishabh Gupta, Rohitash Chandra

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.

📄 PDF Abstract BibTeX arXiv:2102.10551

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sydney-machine-learning/airpollution_deeplearning 공식 구현

Tasks

DecoderTime SeriesTime Series Analysis

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