A Quality Control Method Based on an Improved Random Forest Algorithm for Surface Air Temperature Observations
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 wer...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2017/8601296 |
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| author | Xiaoling Ye Xing Yang Xiong Xiong Yunpei Shen Man Hao Rong Gu |
| author_facet | Xiaoling Ye Xing Yang Xiong Xiong Yunpei Shen Man Hao Rong Gu |
| author_sort | Xiaoling Ye |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-413e21409f914dbe8287d4cce5d87662 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-413e21409f914dbe8287d4cce5d876622025-02-03T01:22:16ZengWileyAdvances in Meteorology1687-93091687-93172017-01-01201710.1155/2017/86012968601296A Quality Control Method Based on an Improved Random Forest Algorithm for Surface Air Temperature ObservationsXiaoling Ye0Xing Yang1Xiong Xiong2Yunpei Shen3Man Hao4Rong Gu5School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaA 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.http://dx.doi.org/10.1155/2017/8601296 |
| spellingShingle | Xiaoling Ye Xing Yang Xiong Xiong Yunpei Shen Man Hao Rong Gu A Quality Control Method Based on an Improved Random Forest Algorithm for Surface Air Temperature Observations Advances in Meteorology |
| title | A Quality Control Method Based on an Improved Random Forest Algorithm for Surface Air Temperature Observations |
| title_full | A Quality Control Method Based on an Improved Random Forest Algorithm for Surface Air Temperature Observations |
| title_fullStr | A Quality Control Method Based on an Improved Random Forest Algorithm for Surface Air Temperature Observations |
| title_full_unstemmed | A Quality Control Method Based on an Improved Random Forest Algorithm for Surface Air Temperature Observations |
| title_short | A Quality Control Method Based on an Improved Random Forest Algorithm for Surface Air Temperature Observations |
| title_sort | quality control method based on an improved random forest algorithm for surface air temperature observations |
| url | http://dx.doi.org/10.1155/2017/8601296 |
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