NDLI: Modeling non-stationary extreme waves using a point process approach and wavelets

Content Provider	Springer Nature Link
Author	Galiatsatou, P. Pris, P.
Copyright Year	2010
Abstract	In the present paper a statistical model for extreme value analysis is developed, considering seasonality. The model is applied to significant wave height data from the N. Aegean Sea. To build this model, a non-stationary point process is used, which incorporates apart from a time varying threshold and harmonic functions with a period of one year, a component μ w(t) estimated through the wavelet transform. The wavelet transform has a dual role in the present study. It detects the significant “periodicities” of the signal by means of the wavelet global and scale-averaged power spectra and then is used to reconstruct the part of the time series, μ w(t), represented by these significant features. A number of candidate models, which incorporate μ w(t) in their location and scale parameters are tried. To avoid overparameterisation, an automatic model selection procedure based on the Akaike information criterion is carried out. The best obtained model is graphically evaluated by means of diagnostic plots. Finally, “aggregated” return levels with return periods of 20, 50 and 100 years, as well as time-dependent quantiles are estimated, combining the results of the wavelet analysis and the Poisson process model, identifying a significant reduction in return level estimation uncertainty, compared to more simple non-stationary models.
Starting Page	165
Ending Page	183
Page Count	19
File Format	PDF
ISSN	14363240
Journal	Stochastic Environmental Research and Risk Assessment
Volume Number	25
Issue Number	2
e-ISSN	14363259
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-12-22
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Wave energy Wavelet Seasonal variations Non-stationarity Extremes Point process Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution Computational Intelligence Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Mathematical Applications in Earth Sciences Probability Theory and Stochastic Processes Math. Application in Environmental Science
Content Type	Text
Resource Type	Article
Subject	Environmental Chemistry Environmental Engineering Water Science and Technology Safety, Risk, Reliability and Quality

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