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• W3132721117 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." @default.
• W3132721117 created "2021-03-01" @default.
• W3132721117 creator A5008205287 @default.
• W3132721117 creator A5043246042 @default.
• W3132721117 creator A5087142595 @default.
• W3132721117 date "2021-02-21" @default.
• W3132721117 modified "2023-09-27" @default.
• W3132721117 title "Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdown" @default.
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