Air Pollution Modelling with Deep Learning: A Review

Year 2018, Volume: 1 Issue: 3, 58 - 62, 02.07.2018

Yasin Akın Ayturan Zeynep Cansu Ayturan Hüseyin Oktay Altun

Abstract

Air pollution is one
of the fundamental environmental problems of the industrialized world due to
its adverse effects on all organisms. Several institutions warn that there
exist serious air pollution in many regions of the world. When all devastating
effects of air pollutants considered, it is crucial to create valid models to
predict air pollution levels in order to determine future concentrations or to
locate pollutant sources. These models may provide policy implications for
governments and central authorities in order to prevent the excessive pollution
levels. Though there are a number of attempts to model pollution levels in the
literature, recent advances in deep learning techniques are promising more
accurate prediction results along with integration of more data.  In this study, a detailed research about
modelling with deep learning architectures on real air pollution data is given.
With the help of this research we attempt to develop air pollution
architectures with deep learning in future and enhance the results further with
insights from recent advances of deep learning research such as Generative
Adversarial Networks (GANs), where two competing networks are working against
each other, one for creating a more realistic data and the other one to predict
the state.   

Keywords

Deep learning, air pollution estimation, artificial neural networks, generative adversarial model

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