Abstract
Air quality index (AQI) is the potential air quality measure for the city dwellers which can help to determine the ambient quality of their surroundings. AQI looks like “One number—One Color—One message” which conveys to the common people to take necessary steps. Prediction of AQI is always giving one step ahead from the current situation. The main objective of our research was to forecast the value of major ambient air pollutants using exponential smoothing which is a simple method and also include the trend and seasonality in the sensed data. In this manuscript, the value of major ambient air pollutants is not only forecasted using single exponential smoothing but also introduce double exponential smoothing technique which allows trend on data. Furthermore, our research will be implemented in a fog-enabled data deployment technique which can reduce network bandwidth and also save data storage cost of remote cloud platform. These predicted data will help to identify the green route in the future urban city.
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Bose, R., Dey, R.K., Roy, S., Sarddar, D. (2020). Time Series Forecasting Using Double Exponential Smoothing for Predicting the Major Ambient Air Pollutants. In: Tuba, M., Akashe, S., Joshi, A. (eds) Information and Communication Technology for Sustainable Development. Advances in Intelligent Systems and Computing, vol 933. Springer, Singapore. https://doi.org/10.1007/978-981-13-7166-0_60
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DOI: https://doi.org/10.1007/978-981-13-7166-0_60
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