A Comparative Study on Utilizing Machine Learning and Ensemble Learning to Classify to Predict Air Quality

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Baby, Basil Karedath
Issue Date
MSc in Data Analytics
Dublin Business School
In this study, I will address air quality forecasting by employing machine learning algorithms to estimate the hourly concentrations of air pollutants (Such as, ozone, particle matter (PM2.5), and sulphur dioxide). Machine learning, one of the most common approaches, is capable of effectively training a model on massive amounts of data by employing large-scale optimization algorithms. Although there are several works that use machine learning to forecast air quality, earlier researches are limited to many years of data and simply train conventional regression models (linear or nonlinear) to predict hourly air pollution concentrations. In this paper, we offer updated models for predicting hourly air pollution concentrations based on previous days' meteorological data by structuring the prediction across 24 hours as a multi-task learning (MTL) issue. This allows us to choose a decent model using various regularization strategies. Air pollution has been a big issue for the general population and governments all around the world.