Full metadata
Title
Deep Learning based Air Quality Prediction with Time Series Sensor Data
Description
ABSTRACT
With the fast development of industry, it brings indelible pollution to the natural environment. As a consequence, the air quality is getting worse which will seriously affect people's health. With such concern, continuous air quality monitoring and prediction are necessary. Traditional air quality monitoring methods cannot use large amount of historical data to make accurate predic-tions. Moreover, the traditional prediction method can only roughly predict the air quality level in a short time. With the development of artificial intelligence al-gorithms [1] and high performance computing, the latest mathematical methods and algorithms are able to generate much more accurate predictions based on long term past data. In this master thesis project, it explore to develop deep learning based air quality prediction based on real sensor network time series air quality data from STAIR system [3].
Date Created
2023
Contributors
- Zhou, Zeming (Author)
- Fan, Deliang (Thesis advisor)
- Cao, Yu (Committee member)
- Yu, Haofei (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
27 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.189444
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2023
Field of study: Electrical Engineering
System Created
- 2023-08-30 10:38:42
System Modified
- 2023-08-30 10:38:46
- 1 year 2 months ago
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