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Support vector regression for time-series : a machine learning approach to predict the air quality
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With the economic and technological development of cities, environment pollution problems are arising, such as, water, noise and air pollution. In particular, air pollution has a direct impact on human health through the exposure of pollutants and particulates, which has increased the interest in air pollution and its impacts among scientific community ((Hvidtfeldt et al., 2018), (Gonzalez et al., 2017) and (Pimpin et al., 2018)). The main causes associated with air pollution are the burn of fossil fuels, agriculture, exhaust from factories and industries, residential heating and natural disasters.
Air quality in United States is a problem that has been addressed for the last three decades, since the creation of the Clean Air Act program. But, although the air quality has relatively improved over the years, air pollution is still a problem: (Caiazzo, Ashok, Waitz, Yim, & Barrett, 2013) referred that total combustion emissions in US are accountable for about 200,000 premature deaths per year, due to PM2.5 concentrations and 10,000 deaths due to Ozone concentration changes, also the American Lung Association estimated that air pollution related illness costs of approximately $37 billion dollars each year in the US, with California alone hitting $15 billion (Holmes-gen & Barrett, 2016). In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. Thus, and in full knowledge of the increasingly pollution derived problems, the importance of accurately forecast air pollutants levels has increased, playing an important role in air quality management and population prevention against pollution hexes.
Descrição
Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
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Genetic Programming USA Environmental Protection Agency Technological development of cities