Application of Machine Learning in Air Pollution
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".
Deadline for manuscript submissions: closed (15 November 2020) | Viewed by 11057
Special Issue Editor
Interests: deep learning; air quality; particulate matter; machine learning; cloud computing; big data
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The air pollution problem has emerged as a global problem beyond the problems of major cities. The OECD warns that urban air pollution will account for the largest proportion of deaths in the future, not water scarcity or poor sanitation. In particular, the results of the WHO survey that 7 million people die early in the world due to fine dust show that the health of citizens is threatened by air pollution. Therefore, we need to make efforts to solve the air pollution problem together across national and urban boundaries. Machine learning is a field of artificial intelligence (AI) that automates model creation for data analysis so that software learns and finds patterns based on data. With the advent of new computing technologies, machine learning today is different from machine learning in the past. With the development of new technologies, including deep learning, various machine learning algorithms are being developed that can be applied to big data analysis faster and faster by repeating complex calculations. In the field of air pollution, this shows the possibility of research expansion, such as monitoring, analysis, and prediction using machine learning. This Special Issue is intended to investigate applications and studies using a variety of machine learning approaches, including deep learning in air pollution. For this Special Issue, we invite submissions that closely interlink air pollution with machine learning, and how machine learning can help to achieve air pollution research goals.
Prof. HwaMin Lee
Guest Editor
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Keywords
- Air pollution
- Machine learning
- Deep learning
- Artificial intelligence
- Air quality
- Particulate matter
- Air pollution management application
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