Application of machine learning and time-series analysis for air pollution prediction
Date Issued
2018-04
Author(s)
Stojov, Vladimir
Koteli, Nikola
Abstract
Medical research studies show that low air quality
can have a direct effect on the increased number of diseases,
especially respiratory defects, but also on the increased mortality
rate in people. Luckily, harmful particles and substances in the
air can easily be detected and measured by using affordable
sensors. The number of this type of sensors deployed in the
city of Skopje, Macedonia continuously grows. The increased
coverage of monitored regions, and the elevated public interest
in solving this problem for obvious reasons, make the prediction
of high levels of air pollution extremely beneficial. According
to the available historical data, the problem of low air quality
is proving to be more serious during the winter, that is during
the heating season. If weather forecast is available, there is an
opportunity to predict the air quality. This work reviews recent
advances in air quality predictions using time-series analysis
techniques, machine learning and deep learning. We proposes and
evaluate two approaches for air quality prediction: combination
of LSTM and convolutional neural networks and one-dimensional
convolutional neural networks. The results show a promising
accuracy of about 78% in predicting the level of air pollution.
can have a direct effect on the increased number of diseases,
especially respiratory defects, but also on the increased mortality
rate in people. Luckily, harmful particles and substances in the
air can easily be detected and measured by using affordable
sensors. The number of this type of sensors deployed in the
city of Skopje, Macedonia continuously grows. The increased
coverage of monitored regions, and the elevated public interest
in solving this problem for obvious reasons, make the prediction
of high levels of air pollution extremely beneficial. According
to the available historical data, the problem of low air quality
is proving to be more serious during the winter, that is during
the heating season. If weather forecast is available, there is an
opportunity to predict the air quality. This work reviews recent
advances in air quality predictions using time-series analysis
techniques, machine learning and deep learning. We proposes and
evaluate two approaches for air quality prediction: combination
of LSTM and convolutional neural networks and one-dimensional
convolutional neural networks. The results show a promising
accuracy of about 78% in predicting the level of air pollution.
Subjects
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