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Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning
Joint Authors
Kim, Yong-Hyuk
Kim, Hye-Jin
Park, Sung Min
Choi, Byung Jin
Moon, Seung-Hyun
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-03-11
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
We propose three quality control (QC) techniques using machine learning that depend on the type of input data used for training.
These include QC based on time series of a single weather element, QC based on time series in conjunction with other weather elements, and QC using spatiotemporal characteristics.
We performed machine learning-based QC on each weather element of atmospheric data, such as temperature, acquired from seven types of IoT sensors and applied machine learning algorithms, such as support vector regression, on data with errors to make meaningful estimates from them.
By using the root mean squared error (RMSE), we evaluated the performance of the proposed techniques.
As a result, the QC done in conjunction with other weather elements had 0.14% lower RMSE on average than QC conducted with only a single weather element.
In the case of QC with spatiotemporal characteristic considerations, the QC done via training with AWS data showed performance with 17% lower RMSE than QC done with only raw data.
American Psychological Association (APA)
Kim, Hye-Jin& Park, Sung Min& Choi, Byung Jin& Moon, Seung-Hyun& Kim, Yong-Hyuk. 2020. Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1138826
Modern Language Association (MLA)
Kim, Hye-Jin…[et al.]. Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1138826
American Medical Association (AMA)
Kim, Hye-Jin& Park, Sung Min& Choi, Byung Jin& Moon, Seung-Hyun& Kim, Yong-Hyuk. Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1138826
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138826