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Języki publikacji
Abstrakty
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
Czasopismo
Rocznik
Tom
Strony
44--50
Opis fizyczny
Bibliogr. 14 poz., rys., tab.
Twórcy
autor
- Department of Maritime Engineering Amirkabir University of Technology Hafez avenue, 14717 Tehran Iran
autor
- Department of Maritime Engineering Amirkabir University of Technology Hafez avenue, 14717 Tehran Iran
Bibliografia
- 1. Iglewicz, B., Hoaglin, D.C.: How to detect and handle outliers. Milwaukee, WI.: ASQC Quality Press, 1993.
- 2. Sun S. Z., LI, H., Sun, H. : Measurement and analysis of coastal waves along the north sea area of China. Polish Maritime Research, 3 (91) 2016, 23, pp. 72-78.
- 3. Whan Lee, J., Park, S. C., Kee Lee, D., Ho Lee, J. : Tsunami arrival time detection system applicable to discontinuous time-series data with outliers. Journal of natural hazards and earth sciences, 2016, 16 (12), pp. 2603-2016.
- 4. Mínguez, R., Reguero, B.G., Luceño, A., Méndez, F.J. : Regression models for outlier identification (hurricanes and typhoons) in wave hindcast databases. Journal of Atmospheric and Oceanic Technology, 2012, 29, pp. 267–285.
- 5. Lucas, C., Muraleedharan, G., Soares, C. G. : Outliers identification in a wave hindcast dataset used for regional frequency analysis. Maritime Technology and Engineering, 2015, pp. 1317-1327.
- 6. Reguero, B.G., Menéndez, M., Méndez, F.J., Mínguez, R., Losada, I. J. : A Global Ocean Wave (GOW) calibrated reanalysis from 1948 onwards. Coastal Engineering, 2012, 65, pp. 38–55.
- 7. Chandola. V., Banerjee, A., Kumar, V. : Anomaly detection – a survey. ACM Comput Surv. 2009, 4 (3), pp. 1–58.
- 8. Barnett, V., Lewis, T. : Outliers in Statistical Data. John Wiley, 3rd edition 1994.
- 9. Zhang, Ji. : Advancements of Outlier Detection: A Survey. ICST Transactions on Scalable Information Systems, 2013, 13 (1), pp. 1-26.
- 10. Muraleedharan, G., Lucas, C., Guedes Soares, C.: Regression quantile models for estimating trends in extreme significant wave heights. J. Ocean Engineering. 2016, 118, pp. 204–215.
- 11. Zhang, K., Hutter, M., Jin, H. : A new local distance-based outlier detection approach for scattered real-world data. Proc. 13th Pacific-Asia Conf. on Knowledge Discovery and Data Mining, 2009, pp. 813-822.
- 12. Chen, Y., Miao, D., Zhang, H. : Neighborhood outlier detection. Expert Systems with Applications, 2010, 37 (12), pp. 8745-8749.
- 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.
- 14. Troncoso, A., Salcedo-Sanz, Casanova-Mateo, S., Riquelme, J.C, C., Prieto, L. : Local models-based regression trees for very short-term wind speed prediction. Renewable Energy, 2015, 81, pp. 589-598.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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