Outlier Detection in Ocean Wave Measurements by Using Unsupervised Data Mining Methods
Outliers are considerably inconsistent and exceptional objects in the data set that do not adapt to expected normal condition. An outlier in wave measurements may be due to experimental and configuration errors, technical defects in equipment, variability in the measurement conditions, rare or unkno...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Sciendo
2018-03-01
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| Series: | Polish Maritime Research |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/pomr-2018-0005 |
| _version_ | 1818871919652372480 |
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| author | Mahmoodi Kumars Ghassemi Hassan |
| author_facet | Mahmoodi Kumars Ghassemi Hassan |
| author_sort | Mahmoodi Kumars |
| collection | DOAJ |
| description | Outliers are considerably inconsistent and exceptional objects in the data set that do not adapt to expected normal condition. An outlier in wave measurements may be due to experimental and configuration errors, technical defects in equipment, variability in the measurement conditions, rare or unknown conditions such as tsunami, windstorm and etc. To improve the accuracy and reliability of an built ocean wave model, or to extract important and valuable information from collected wave data, detecting of outlying observations in wave measurements is very important. In this study, three typical outlier detection algorithms:Box-plot (BP), Local Distance-based Outlier Factor (LDOF), and Local Outlier Factor (LOF) methods are used to detect outliers in significant wave height (Hs) records. The historical wave data are taken from National Data Buoy Center (NDBC). Finally, those data points are considered as outlier identified by at least two methods which are presented and discussed. Then, Hs prediction has been modelled with and without the presence of outliers by using Regression trees (RTs). |
| first_indexed | 2024-12-19T12:30:34Z |
| format | Article |
| id | doaj.art-7fdda42ac43f42e5b9aeec2c75c263e7 |
| institution | Directory Open Access Journal |
| issn | 2083-7429 |
| language | English |
| last_indexed | 2024-12-19T12:30:34Z |
| publishDate | 2018-03-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Polish Maritime Research |
| spelling | doaj.art-7fdda42ac43f42e5b9aeec2c75c263e72022-12-21T20:21:24ZengSciendoPolish Maritime Research2083-74292018-03-01251445010.2478/pomr-2018-0005pomr-2018-0005Outlier Detection in Ocean Wave Measurements by Using Unsupervised Data Mining MethodsMahmoodi Kumars0Ghassemi Hassan1Amirkabir University of Technology, Tehran, IranAmirkabir University of Technology, Tehran, IranOutliers are considerably inconsistent and exceptional objects in the data set that do not adapt to expected normal condition. An outlier in wave measurements may be due to experimental and configuration errors, technical defects in equipment, variability in the measurement conditions, rare or unknown conditions such as tsunami, windstorm and etc. To improve the accuracy and reliability of an built ocean wave model, or to extract important and valuable information from collected wave data, detecting of outlying observations in wave measurements is very important. In this study, three typical outlier detection algorithms:Box-plot (BP), Local Distance-based Outlier Factor (LDOF), and Local Outlier Factor (LOF) methods are used to detect outliers in significant wave height (Hs) records. The historical wave data are taken from National Data Buoy Center (NDBC). Finally, those data points are considered as outlier identified by at least two methods which are presented and discussed. Then, Hs prediction has been modelled with and without the presence of outliers by using Regression trees (RTs).https://doi.org/10.2478/pomr-2018-0005ocean wave datadata miningoutlier detectiondata correction |
| spellingShingle | Mahmoodi Kumars Ghassemi Hassan Outlier Detection in Ocean Wave Measurements by Using Unsupervised Data Mining Methods Polish Maritime Research ocean wave data data mining outlier detection data correction |
| title | Outlier Detection in Ocean Wave Measurements by Using Unsupervised Data Mining Methods |
| title_full | Outlier Detection in Ocean Wave Measurements by Using Unsupervised Data Mining Methods |
| title_fullStr | Outlier Detection in Ocean Wave Measurements by Using Unsupervised Data Mining Methods |
| title_full_unstemmed | Outlier Detection in Ocean Wave Measurements by Using Unsupervised Data Mining Methods |
| title_short | Outlier Detection in Ocean Wave Measurements by Using Unsupervised Data Mining Methods |
| title_sort | outlier detection in ocean wave measurements by using unsupervised data mining methods |
| topic | ocean wave data data mining outlier detection data correction |
| url | https://doi.org/10.2478/pomr-2018-0005 |
| work_keys_str_mv | AT mahmoodikumars outlierdetectioninoceanwavemeasurementsbyusingunsuperviseddataminingmethods AT ghassemihassan outlierdetectioninoceanwavemeasurementsbyusingunsuperviseddataminingmethods |