Outlier Detection and Correction for Monitoring Data of Water Quality Based on Improved VMD and LSSVM
To improve the detection rate and reduce the correction error of abnormal data for water quality, an outlier detection and correction method is proposed based on the improved Variational Mode Decomposition (improved VMD) and Least Square Support Vector Machine (LSSVM) algorithms. The correlation coe...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/9643921 |
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| _version_ | 1832545466528563200 |
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| author | Guangpei Sun Peng Jiang Huan Xu Shanen Yu Dong Guo Guang Lin Hui Wu |
| author_facet | Guangpei Sun Peng Jiang Huan Xu Shanen Yu Dong Guo Guang Lin Hui Wu |
| author_sort | Guangpei Sun |
| collection | DOAJ |
| description | To improve the detection rate and reduce the correction error of abnormal data for water quality, an outlier detection and correction method is proposed based on the improved Variational Mode Decomposition (improved VMD) and Least Square Support Vector Machine (LSSVM) algorithms. The correlation coefficient is introduced, for solving the optimal parameter k of VMD algorithm, and an improved VMD algorithm is obtained. Combined with LSSVM algorithm, the outliers of water quality can be detected and repaired. This method is applied for the detection and correction of water quality monitoring outliers using dissolved oxygen which is retrieved from the water quality monitoring station in Hangzhou, Zhejiang Province, China. The result shows that the improved VMD algorithm is of higher detection rate and lower error rate than those of Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD). The LSSVM algorithm increases the fitting accuracy and decreases correction error in comparison with SVM and BP neural network, which provides important references for the implementation of environmental protection measures. |
| format | Article |
| id | doaj-art-c1581db4dad841dc91a34f0bb7097143 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c1581db4dad841dc91a34f0bb70971432025-02-03T07:25:51ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/96439219643921Outlier Detection and Correction for Monitoring Data of Water Quality Based on Improved VMD and LSSVMGuangpei Sun0Peng Jiang1Huan Xu2Shanen Yu3Dong Guo4Guang Lin5Hui Wu6College of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaCollege of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaCollege of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaCollege of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaCollege of Electrical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, ChinaZhejiang Provincial Environmental Monitoring Center, Hangzhou 310018, ChinaFuzhou Fuguang Water Technology Co., Ltd., Fuzhou 350000, ChinaTo improve the detection rate and reduce the correction error of abnormal data for water quality, an outlier detection and correction method is proposed based on the improved Variational Mode Decomposition (improved VMD) and Least Square Support Vector Machine (LSSVM) algorithms. The correlation coefficient is introduced, for solving the optimal parameter k of VMD algorithm, and an improved VMD algorithm is obtained. Combined with LSSVM algorithm, the outliers of water quality can be detected and repaired. This method is applied for the detection and correction of water quality monitoring outliers using dissolved oxygen which is retrieved from the water quality monitoring station in Hangzhou, Zhejiang Province, China. The result shows that the improved VMD algorithm is of higher detection rate and lower error rate than those of Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD). The LSSVM algorithm increases the fitting accuracy and decreases correction error in comparison with SVM and BP neural network, which provides important references for the implementation of environmental protection measures.http://dx.doi.org/10.1155/2019/9643921 |
| spellingShingle | Guangpei Sun Peng Jiang Huan Xu Shanen Yu Dong Guo Guang Lin Hui Wu Outlier Detection and Correction for Monitoring Data of Water Quality Based on Improved VMD and LSSVM Complexity |
| title | Outlier Detection and Correction for Monitoring Data of Water Quality Based on Improved VMD and LSSVM |
| title_full | Outlier Detection and Correction for Monitoring Data of Water Quality Based on Improved VMD and LSSVM |
| title_fullStr | Outlier Detection and Correction for Monitoring Data of Water Quality Based on Improved VMD and LSSVM |
| title_full_unstemmed | Outlier Detection and Correction for Monitoring Data of Water Quality Based on Improved VMD and LSSVM |
| title_short | Outlier Detection and Correction for Monitoring Data of Water Quality Based on Improved VMD and LSSVM |
| title_sort | outlier detection and correction for monitoring data of water quality based on improved vmd and lssvm |
| url | http://dx.doi.org/10.1155/2019/9643921 |
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