A fuzzy quantification approach of uncertainties in an extreme wave height modeling

ZHANG Yi; CAO Yingyi

doi:10.1007/s13131-015-0636-5

Article Contents

Article Navigation > Acta Oceanologica Sinica > 2015 > 34(3): 90-98

ZHANG Yi, CAO Yingyi. A fuzzy quantification approach of uncertainties in an extreme wave height modeling[J]. Acta Oceanologica Sinica, 2015, 34(3): 90-98. doi: 10.1007/s13131-015-0636-5

Citation:

ZHANG Yi, CAO Yingyi. A fuzzy quantification approach of uncertainties in an extreme wave height modeling[J]. Acta Oceanologica Sinica, 2015, 34(3): 90-98. doi: 10.1007/s13131-015-0636-5

ZHANG Yi, CAO Yingyi. A fuzzy quantification approach of uncertainties in an extreme wave height modeling[J]. Acta Oceanologica Sinica, 2015, 34(3): 90-98. doi: 10.1007/s13131-015-0636-5

Citation:

ZHANG Yi, CAO Yingyi. A fuzzy quantification approach of uncertainties in an extreme wave height modeling[J]. Acta Oceanologica Sinica, 2015, 34(3): 90-98. doi: 10.1007/s13131-015-0636-5

PDF( 1107 KB)

A fuzzy quantification approach of uncertainties in an extreme wave height modeling

doi: 10.1007/s13131-015-0636-5

ZHANG Yi^1
,,
CAO Yingyi²

1.
Division of Infrastructure Systems and Maritime Studies, School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore
2.
College of Preschool Education, Zibo Normal College, Zibo 255130, China

Received Date: 2014-02-24
Rev Recd Date: 2014-10-21

Abstract

Abstract

A non-traditional fuzzy quantification method is presented in the modeling of an extreme significant wave height. First, a set of parametric models are selected to fit time series data for the significant wave height and the extrapolation for extremes are obtained based on high quantile estimations. The quality of these results is compared and discussed. Then, the proposed fuzzy model, which combines Poisson process and generalized Pareto distribution (GPD) model, is applied to characterizing the wave extremes in the time series data. The estimations for a long-term return value are considered as time-varying as a threshold is regarded as non-stationary. The estimated intervals coupled with the fuzzy theory are then introduced to construct the probability bounds for the return values. This nontraditional model is analyzed in comparison with the traditional model in the degree of conservatism for the long-term estimate. The impact on the fuzzy bounds of extreme estimations from the non stationary effect in the proposed model is also investigated.
- offshore engineering,
- extreme value distribution,
- wave height,
- peak over threshold,
- fuzzy set,
- Pareto distribution

FullText(HTML)

References(16)

References

Beer M, Zhang Y, Quek S T, et al. 2013. Reliability analysis with scarce information: comparing alternative approaches in a geotechnical engineering context. Structural Safety, 41: 1-10

Chen T C, Yoon J. 2002. Interdecadal variation of the North Pacific wintertime blocking. Monthly Weather Review, 130(12): 3136-3143

Coles S G. 2001. An Introduction to Statistical Modeling of Extreme Values. New York: Springer

Corti S, Giannini A, Tibaldi S, et al. 1997. Patterns of low-frequency variability in a three-level quasi-geostrophic model. Climate Dynamics, 13(12): 883-904

Ewans K, Jonathan P. 2014. Evaluating environmental joint extremes for the offshore industry using the conditional extremes model. Journal of Marine Systems, 130: 124-130

Guedes Soares C, Weisse R, Alvarez E, et al. 2002. A 40 year hindcast of wind, sea level and waves in European waters. In: Proceedings of the 21st International Conference on Offshore Mechanics and Arctic Engineering. United States: American Society of Mechanical Engineers, 1-7

Harrison G P, Wallace A R. 2005. Sensitivity of wave energy to climate change. IEEE Transactions on Energy Conversion, 20(4): 870-877

Möller B, Beer M. 2004. Fuzzy Randomness: Uncertainty in Civil Engineering and Computational Mechanics. Berlin: Springer

Morton I D, Bowers J, Mould G. 1997. Estimating return period wave heights and wind speeds using a seasonal point process model. Coastal Engineering, 31(14): 305-326

Pickands J. 1975. Statistical inference using extreme order statistics. The Annals of Statistics, 3(1): 119-131

Smith R L. 2001. Environmental Statistics. Technical Report. Department of Statistics, University of North Carolina, Chapel Hill

Wang X L, Zwiers F W, Swail V R. 2004. North Atlantic Ocean wave climate change scenarios for the twenty-first century. Journal of Climate, 17(12): 2368-2383

Zadeh L A. 1965. Fuzzy sets. Information and Control, 8(3): 338-353

Zhang Yi, Lam J S L. 2014. Non-conventional modeling of extreme significant wave height through random sets. Acta Oceanologica

Sinica, 33(7): 125-130

Zheng Chongwei, Shao Longtan, Shi Wenli, et al. 2014. An assessment of global ocean wave energy resources over the last 45 a. Acta Oceanologica Sinica, 33(1): 92-101

Relative Articles

Supplements(0)

Cited By

Proportional views