Abstract
Outdoor air pollution has emerged as a serious threat to public health across the globe. Air quality monitoring and forecasting are required to provide the policy makers a scientific basis for formulating a robust policy on abatement of air pollution. Moreover, if air pollution forecasts are issued to the public, they can take preventive measures to minimize their exposure to unsafe levels of air pollutants. In this paper, an intelligent air pollution prediction system using Extreme Learning Machine (ELM) has been proposed to predict the air quality index for five pollutants (PM10, PM2.5, NO2, CO, O3) for the next day. It is found that the prediction of ELM-based proposed system is better than the existing air pollution prediction systems.
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Bisht, M., Seeja, K.R. (2018). Air Pollution Prediction Using Extreme Learning Machine: A Case Study on Delhi (India). In: Somani, A., Srivastava, S., Mundra, A., Rawat, S. (eds) Proceedings of First International Conference on Smart System, Innovations and Computing. Smart Innovation, Systems and Technologies, vol 79. Springer, Singapore. https://doi.org/10.1007/978-981-10-5828-8_18
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DOI: https://doi.org/10.1007/978-981-10-5828-8_18
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