ZHANG Yi, LAM Jasmine Siu Lee. Non-conventional modeling of extreme significant wave height through random sets[J]. Acta Oceanologica Sinica, 2014, 33(7): 125-130. doi: 10.1007/s13131-014-0508-4
Citation: ZHANG Yi, LAM Jasmine Siu Lee. Non-conventional modeling of extreme significant wave height through random sets[J]. Acta Oceanologica Sinica, 2014, 33(7): 125-130. doi: 10.1007/s13131-014-0508-4

Non-conventional modeling of extreme significant wave height through random sets

doi: 10.1007/s13131-014-0508-4
  • Received Date: 2013-02-05
  • Rev Recd Date: 2014-01-22
  • The analysis and design of offshore structures necessitates the consideration of wave loads. Realistic modeling of wave loads is particularly important to ensure reliable performance of these structures. Among the available methods for the modeling of the extreme significant wave height on a statistical basis, the peak over threshold method has attracted most attention. This method employs Poisson process to characterize time-varying properties in the parameters of an extreme value distribution. In this paper, the peak over threshold method is reviewed and extended to account for subjectivity in the modeling. The freedom in selecting the threshold and the time span to separate extremes from the original time series data is incorporated as imprecision in the model. This leads to an extension from random variables to random sets in the probabilistic model for the extreme significant wave height. The extended model is also applied to different periods of the sampled data to evaluate the significance of the climatic conditions on the uncertainties of the parameters.
  • loading
  • Bermudez P Z, Kotz S. 2010. Parameter estimation of the generalizedPareto distribution-Part Ⅰ, Ⅱ. Journal of Statistical Planning andInference, 140(6): 1353-1397
    Coles S G. 2001. An Introduction to Statistical Modeling of Extreme Values.New York: Springer
    Goutsias J, Mashler R P S, Nguyen H T. 1997. Random sets. New York:Springer
    Hosking J R M. 1990. L-moments: analysis and estimation of distributionsusing linear combinations of order statistics. Journal of theRoyal Statistical Society, 52: 105-124
    Jonathan P, Ewans K. 2011. Modeling the seasonality of extreme wavesin the Gulf of Mexico. Journal of Offshore Mechanics and ArcticEngineering, 133: 0211041-49
    Mackay E B L, Challenor P G, Bahaj A B S. 2011. A comparison of estimatorsfor the generalised Pareto distribution. Ocean Engineering,38(11-12): 1338-1346
    Méndez F J, Menéndez M, Luceño A, et al. 2006. Estimation of the longtermvariability of extreme significant wave height using a timedependentPeak Over Threshold model. Journal of GeophysicalResearch, 111: C07024
    Muraleedharan G, Rao A D, Kurup P G, et al. 2007. Modified Weibulldistribution for maximum and significant wave height simulationand prediction. Coastal Engineering, 54(8): 630-638
    Naess A, Clausen P H. 2001. Combination of the peaks-over-thresholdand bootstrapping methods for extreme value prediction. StructuralSafety, 23(4): 315-330
    Pickands J. 1975. Statistical inference using extreme order statistics.Annals of Statistics, 3(1): 119-131
    Ruggiero P, Komar P D, Allan J C. 2010. Increasing wave heights andextreme value projections: The wave climate of the U.S. PacificNorthwest. Coastal Engineering, 57(5): 539-552
    Shafer G. 1976. A Mathematical Theory of Evidence. Princeton: PrincetonUniversity Press
    Smith R L. 2001. Environmental Statistics, Technical Report. NorthCarolina: Chapel Hill
    Zhang Yi. 2013. Modeling time varying and multivariate environmentalconditions for extreme load prediction on offshore structures ina reliability perspective [dissertation]. Singapore: National Universityof Singapore
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (1906) PDF downloads(1080) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return