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OUTLIER DETECTION IN OCEAN WAVE MEASUREMENTS BY USING UNSUPERVISED DATA MINING METHODS

Abstract

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 valuableinformation 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).

Keywords:

ocean wave data, data mining, outlier detection, data correction

Details

Issue
Vol. 25 No. 1(97) (2018)
Section
Latest Articles
Published
11-04-2018
DOI:
https://doi.org/10.2478/pomr-2018-0005
Licencja:
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Open Access License

This journal provides immediate open access to its content under the Creative Commons BY 4.0 license. Authors who publish with this journal retain all copyrights and agree to the terms of the CC BY 4.0 license.

 

Authors

  • Hassan Ghassemi

    Amirkabir University of Technology, Department of Maritime Engineering
  • Kumars Mahmoodi

    Amirkabir University of Technology, Department of Maritime Engineering

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