Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques
The aim of this study is to evaluate the filtering techniques which can remove the noise involved in the time series. For this, Logistic series which is chaotic series and radar rainfall series are used for the evaluation of low-pass filter (LF) and Kalman filter (KF). The noise is added to Logistic...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2014/517571 |
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| author | Soojun Kim Huiseong Noh Narae Kang Keonhaeng Lee Yonsoo Kim Sanghun Lim Dong Ryul Lee Hung Soo Kim |
| author_facet | Soojun Kim Huiseong Noh Narae Kang Keonhaeng Lee Yonsoo Kim Sanghun Lim Dong Ryul Lee Hung Soo Kim |
| author_sort | Soojun Kim |
| collection | DOAJ |
| description | The aim of this study is to evaluate the filtering techniques which can remove the noise involved in the time series. For this, Logistic series which is chaotic series and radar rainfall series are used for the evaluation of low-pass filter (LF) and Kalman filter (KF). The noise is added to Logistic series by considering noise level and the noise added series is filtered by LF and KF for the noise reduction. The analysis for the evaluation of LF and KF techniques is performed by the correlation coefficient, standard error, the attractor, and the BDS statistic from chaos theory. The analysis result for Logistic series clearly showed that KF is better tool than LF for removing the noise. Also, we used the radar rainfall series for evaluating the noise reduction capabilities of LF and KF. In this case, it was difficult to distinguish which filtering technique is better way for noise reduction when the typical statistics such as correlation coefficient and standard error were used. However, when the attractor and the BDS statistic were used for evaluating LF and KF, we could clearly identify that KF is better than LF. |
| format | Article |
| id | doaj-art-58ab739109514ce5a59e3dfe97fb3830 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-58ab739109514ce5a59e3dfe97fb38302025-02-03T06:06:14ZengWileyAdvances in Meteorology1687-93091687-93172014-01-01201410.1155/2014/517571517571Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering TechniquesSoojun Kim0Huiseong Noh1Narae Kang2Keonhaeng Lee3Yonsoo Kim4Sanghun Lim5Dong Ryul Lee6Hung Soo Kim7Columbia Water Center, Columbia University, New York, NY 10027, USADepartment of Civil Engineering, Inha University, Incheon 402-751, Republic of KoreaDepartment of Civil Engineering, Inha University, Incheon 402-751, Republic of KoreaWater Resources Research Division, Korea Institute of Civil Engineering and Building Technology (KICT),Goyang 411-712, Republic of KoreaDepartment of Civil Engineering, Inha University, Incheon 402-751, Republic of KoreaWater Resources Research Division, Korea Institute of Civil Engineering and Building Technology (KICT),Goyang 411-712, Republic of KoreaWater Resources Research Division, Korea Institute of Civil Engineering and Building Technology (KICT),Goyang 411-712, Republic of KoreaDepartment of Civil Engineering, Inha University, Incheon 402-751, Republic of KoreaThe aim of this study is to evaluate the filtering techniques which can remove the noise involved in the time series. For this, Logistic series which is chaotic series and radar rainfall series are used for the evaluation of low-pass filter (LF) and Kalman filter (KF). The noise is added to Logistic series by considering noise level and the noise added series is filtered by LF and KF for the noise reduction. The analysis for the evaluation of LF and KF techniques is performed by the correlation coefficient, standard error, the attractor, and the BDS statistic from chaos theory. The analysis result for Logistic series clearly showed that KF is better tool than LF for removing the noise. Also, we used the radar rainfall series for evaluating the noise reduction capabilities of LF and KF. In this case, it was difficult to distinguish which filtering technique is better way for noise reduction when the typical statistics such as correlation coefficient and standard error were used. However, when the attractor and the BDS statistic were used for evaluating LF and KF, we could clearly identify that KF is better than LF.http://dx.doi.org/10.1155/2014/517571 |
| spellingShingle | Soojun Kim Huiseong Noh Narae Kang Keonhaeng Lee Yonsoo Kim Sanghun Lim Dong Ryul Lee Hung Soo Kim Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques Advances in Meteorology |
| title | Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques |
| title_full | Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques |
| title_fullStr | Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques |
| title_full_unstemmed | Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques |
| title_short | Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques |
| title_sort | noise reduction analysis of radar rainfall using chaotic dynamics and filtering techniques |
| url | http://dx.doi.org/10.1155/2014/517571 |
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