Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning
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 characte...
Main Authors: | Hye-Jin Kim, Sung Min Park, Byung Jin Choi, Seung-Hyun Moon, Yong-Hyuk Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2020-01-01
|
Series: | Computational Intelligence and Neuroscience |
Online Access: | http://dx.doi.org/10.1155/2020/7980434 |
Similar Items
-
Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning
by: Yong-Hyuk Kim, et al.
Published: (2016-01-01) -
An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search
by: Yong-Wook Nam, et al.
Published: (2020-11-01) -
Error Correction of Meteorological Data Obtained with Mini-AWSs Based on Machine Learning
by: Ji-Hun Ha, et al.
Published: (2018-01-01) -
Detection of Precipitation and Fog Using Machine Learning on Backscatter Data from Lidar Ceilometer
by: Yong-Hyuk Kim, et al.
Published: (2020-09-01) -
Atmospheric Error Correction of the Laser Beam Ranging
by: J. Saydi, et al.
Published: (2014-01-01)