A Quality Control Method Based on an Improved Random Forest Algorithm for Surface Air Temperature Observations
Joint Authors
Xiong, Xiong
Ye, XiaoLing
Yang, Xing
Shen, Yunpei
Hao, Man
Gu, Rong
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-10
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
A spatial quality control method, ARF, is proposed.
The ARF method incorporates the optimization ability of the artificial fish swarm algorithm and the random forest regression function to provide quality control for multiple surface air temperature stations.
Surface air temperature observations were recorded at stations in mountainous and plain regions and at neighboring stations to test the performance of the method.
Observations from 2005 to 2013 were used as a training set, and observations from 2014 were used as a testing set.
The results indicate that the ARF method is able to identify inaccurate observations; and it has a higher rate of detection, lower rate of change for the quality control parameters, and fewer type I errors than traditional methods.
Notably, the ARF method yielded low performance indexes in areas with complex terrain, where traditional methods were considerably less effective.
In addition, for stations near the ocean without sufficient neighboring stations, different neighboring stations were used to test the different methods.
Whereas the traditional methods were affected by station distribution, the ARF method exhibited fewer errors and higher stability.
Thus, the method is able to effectively reduce the effects of geographical factors on spatial quality control.
American Psychological Association (APA)
Ye, XiaoLing& Yang, Xing& Xiong, Xiong& Shen, Yunpei& Hao, Man& Gu, Rong. 2017. A Quality Control Method Based on an Improved Random Forest Algorithm for Surface Air Temperature Observations. Advances in Meteorology،Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1122953
Modern Language Association (MLA)
Ye, XiaoLing…[et al.]. A Quality Control Method Based on an Improved Random Forest Algorithm for Surface Air Temperature Observations. Advances in Meteorology No. 2017 (2017), pp.1-15.
https://search.emarefa.net/detail/BIM-1122953
American Medical Association (AMA)
Ye, XiaoLing& Yang, Xing& Xiong, Xiong& Shen, Yunpei& Hao, Man& Gu, Rong. A Quality Control Method Based on an Improved Random Forest Algorithm for Surface Air Temperature Observations. Advances in Meteorology. 2017. Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1122953
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1122953