Outlier detection in ocean wave measurements by using unsupervised data mining methods

• Autorzy: Mahmoodi K. , Ghassemi H.

Identyfikatory

DOI

10.2478/pomr-2018-0005

• Języki publikacji: EN

Abstrakty

EN

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).

Słowa kluczowe

EN

ocean wave data; data mining; outlier detection; data correction

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2018
• Tom: nr 1
• Strony: 44--50
• Opis fizyczny: Bibliogr. 14 poz., rys., tab.

Twórcy

autor

Mahmoodi K.

• Department of Maritime Engineering Amirkabir University of Technology Hafez avenue, 14717 Tehran Iran

autor

Ghassemi H.

• Department of Maritime Engineering Amirkabir University of Technology Hafez avenue, 14717 Tehran Iran

Bibliografia

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• 13. Breunig, M. M., Kriegel, H.-P., Ng, R. T., et al.: LOF: Identifying density-based local outliers. In W. Chen, J. F. Naughton, & P. A. Bernstein (Eds.), Proceedings of the ACM SIGMOD international conference on management of data, ACM Press , Dallas, Texas , 2000, pp. 93–104.
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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).