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Time-Series Based Prediction of Air Quality Index Using Various Machine Learning Models

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Decision Intelligence Solutions (InCITe 2023)

Abstract

Accurate prediction of the Air Quality Index (AQI) is of paramount importance as the negative health impact of poor air quality on humans has been widely established. The paper proposes an efficient model for AQI prediction based on the transformer algorithm. This model is compared with the widely used RNN-LSTM and regression model and outperforms both. Real-time data on pollutant concentration and meteorological data for the Anand Vihar, Delhi, India monitoring station from November 1, 2017, till August 6, 2022, has been used for analysis. From the data available for 11 pollutants, the AQI is determined as per the Central Pollution Control Board’s (CPCB’s) formula. The paper aims to predict the AQI along with the levels of PM2.5, CO, and PM10 pollutants. RMSE and MAE for PM2.5 evaluated using transformer are 17.74 and 11.15, respectively, best amongst all the models. Prior and accurate knowledge about Air Quality Index levels is directly relevant for policy-makers and the population at large so that the administrators may implement corrective measures. As the concept of smart cities is rapidly taking shape, the residents of such cities have the right to know about the air quality they are likely to breathe in the near future so that smart responses may be planned well in advance.

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Acknowledgements

The authors thank Dr. Geetika Jain Saxena, Associate Professor at the University of Delhi, for her valuable technical inputs.

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Correspondence to Ishita Pundir .

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Pundir, I., Aggarwal, N., Singh, S. (2023). Time-Series Based Prediction of Air Quality Index Using Various Machine Learning Models. In: Hasteer, N., McLoone, S., Khari, M., Sharma, P. (eds) Decision Intelligence Solutions. InCITe 2023. Lecture Notes in Electrical Engineering, vol 1080. Springer, Singapore. https://doi.org/10.1007/978-981-99-5994-5_7

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