Abstract
Air Pollution is one of the major problems in today’s world. Automobiles, factories, power plants etc., have made human life easy, but we are compromising the environment. Air pollution is one of the negative impacts that come from these developments. An indexing system has been developed for quantitative analysis of air quality, known as Air quality index. AQI’s value depends on various pollutant values such as PM (Particulate matter), CO, NH3, NO2, H2S etc. Based on past data of AQI, predictions can be done for future AQI values. Significant challenges encountered in AQI monitoring are accuracy of forecasted value and indoor AQI sensing nodes with power efficiency. In this paper, we developed an indoor IoT-based AQI Monitoring sensing node to get the value of the above pollutants in the environment. With results we created a data set for forecasting AQI value. For better accuracy we applied SARIMAX and got better results from other forecasting methods such as ANN and RNN.
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Khare, M.D., Sagar, K.S., Sachdeva, S., Prakash, C. (2022). Forecasting AQI Data with IoT Enabled Indoor Air Quality Monitoring System. In: Sachdeva, S., Watanobe, Y., Bhalla, S. (eds) Big-Data-Analytics in Astronomy, Science, and Engineering. BDA 2021. Lecture Notes in Computer Science(), vol 13167. Springer, Cham. https://doi.org/10.1007/978-3-030-96600-3_11
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DOI: https://doi.org/10.1007/978-3-030-96600-3_11
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