Abstract
Air quality is a major issue that has been affecting the human health, flora, fauna and ecosystems since long. Vehicular pollution, burning of plastics, demolition and construction activities can be considered as the main reasons for degrading air quality. Therefore, it is necessary to devise a model using which we can analyse the air quality trends regularly on monthly and even seasonal basis. In this study, time series models are discussed to analyse future air quality used in modelling and forecasting monthly future air quality in New Delhi, India. By using time series, the study aims to analyse the air quality, and hence, predict the values for the future using ARIMA model and help in improving or at least controlling the degrading air quality. ARIMA stands for autoregressive integrated moving average model that is capable of analysing and representing stationary as well as non-stationary time series. Air pollutants data is analysed on daily basis using time series analysis. Comparison is made between the predicted and observed values of SO2 and NO2. By using ARIMA model, we get satisfactory and reliable results. It will help in getting information and thereon taking quick actions to monitor and control before the conditions get worse. The evaluation of performance is done by calculating mean square error, mean absolute error and root mean square error.
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Gourav, Rekhi, J.K., Nagrath, P., Jain, R. (2020). Forecasting Air Quality of Delhi Using ARIMA Model. In: Jain, V., Chaudhary, G., Taplamacioglu, M., Agarwal, M. (eds) Advances in Data Sciences, Security and Applications. Lecture Notes in Electrical Engineering, vol 612. Springer, Singapore. https://doi.org/10.1007/978-981-15-0372-6_25
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DOI: https://doi.org/10.1007/978-981-15-0372-6_25
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