Matches in SemOpenAlex for { <https://semopenalex.org/work/W2990268370> ?p ?o ?g. }
Showing items 1 to 77 of
77
with 100 items per page.
- W2990268370 abstract "Abstract An important question in the context of rogue waves is whether the statistical properties of individual waves, and in particular the probability of extreme and rogue waves, can be linked to the properties of the underlying wave spectrum of the relevant sea state. It has been suggested that a narrow wave spectrum (in frequency or direction) combined with a large wave steepness may lead to increased occurrence of extreme waves. Parameters based on the ratio of the wave steepness to the spectral band-widths have therefore been suggested as indicators of increased probability of extreme waves. However, for realistic ocean conditions the success of such parameters seems to be questionable. In this paper, we investigate relations between short-time wave statistics and wave spectral properties by using machine learning methods that can take a much wider range of spectral properties, or even the entire directional wave spectrum, into account. Numerical simulations with a nonlinear wave model that provides phase-resolved wave information are combined with wave spectra from a spectral wave model. Machine learning methods are then employed to investigate how well the wave statistics can be predicted from knowledge about the wave spectrum. The results are discussed in the context of existing parameters suggested as indicators of rogue waves, as well as with respect to potential warning against sea states in which extreme waves are expected to occur, based on wave-forecast from spectral wave models." @default.
- W2990268370 created "2019-12-05" @default.
- W2990268370 creator A5048414109 @default.
- W2990268370 creator A5053003917 @default.
- W2990268370 date "2019-06-09" @default.
- W2990268370 modified "2023-09-26" @default.
- W2990268370 title "Predicting Extreme Waves From Wave Spectral Properties Using Machine Learning" @default.
- W2990268370 doi "https://doi.org/10.1115/omae2019-96061" @default.
- W2990268370 hasPublicationYear "2019" @default.
- W2990268370 type Work @default.
- W2990268370 sameAs 2990268370 @default.
- W2990268370 citedByCount "3" @default.
- W2990268370 countsByYear W29902683702020 @default.
- W2990268370 countsByYear W29902683702021 @default.
- W2990268370 countsByYear W29902683702022 @default.
- W2990268370 crossrefType "proceedings-article" @default.
- W2990268370 hasAuthorship W2990268370A5048414109 @default.
- W2990268370 hasAuthorship W2990268370A5053003917 @default.
- W2990268370 hasConcept C120665830 @default.
- W2990268370 hasConcept C121332964 @default.
- W2990268370 hasConcept C121864883 @default.
- W2990268370 hasConcept C127313418 @default.
- W2990268370 hasConcept C151730666 @default.
- W2990268370 hasConcept C154945302 @default.
- W2990268370 hasConcept C155761240 @default.
- W2990268370 hasConcept C158622935 @default.
- W2990268370 hasConcept C165082838 @default.
- W2990268370 hasConcept C189201455 @default.
- W2990268370 hasConcept C24890656 @default.
- W2990268370 hasConcept C2779343474 @default.
- W2990268370 hasConcept C2780150128 @default.
- W2990268370 hasConcept C2781147146 @default.
- W2990268370 hasConcept C30475298 @default.
- W2990268370 hasConcept C41008148 @default.
- W2990268370 hasConcept C44886760 @default.
- W2990268370 hasConcept C50644808 @default.
- W2990268370 hasConcept C62520636 @default.
- W2990268370 hasConcept C84174578 @default.
- W2990268370 hasConcept C97355855 @default.
- W2990268370 hasConceptScore W2990268370C120665830 @default.
- W2990268370 hasConceptScore W2990268370C121332964 @default.
- W2990268370 hasConceptScore W2990268370C121864883 @default.
- W2990268370 hasConceptScore W2990268370C127313418 @default.
- W2990268370 hasConceptScore W2990268370C151730666 @default.
- W2990268370 hasConceptScore W2990268370C154945302 @default.
- W2990268370 hasConceptScore W2990268370C155761240 @default.
- W2990268370 hasConceptScore W2990268370C158622935 @default.
- W2990268370 hasConceptScore W2990268370C165082838 @default.
- W2990268370 hasConceptScore W2990268370C189201455 @default.
- W2990268370 hasConceptScore W2990268370C24890656 @default.
- W2990268370 hasConceptScore W2990268370C2779343474 @default.
- W2990268370 hasConceptScore W2990268370C2780150128 @default.
- W2990268370 hasConceptScore W2990268370C2781147146 @default.
- W2990268370 hasConceptScore W2990268370C30475298 @default.
- W2990268370 hasConceptScore W2990268370C41008148 @default.
- W2990268370 hasConceptScore W2990268370C44886760 @default.
- W2990268370 hasConceptScore W2990268370C50644808 @default.
- W2990268370 hasConceptScore W2990268370C62520636 @default.
- W2990268370 hasConceptScore W2990268370C84174578 @default.
- W2990268370 hasConceptScore W2990268370C97355855 @default.
- W2990268370 hasLocation W29902683701 @default.
- W2990268370 hasOpenAccess W2990268370 @default.
- W2990268370 hasPrimaryLocation W29902683701 @default.
- W2990268370 hasRelatedWork W1838520085 @default.
- W2990268370 hasRelatedWork W1909732448 @default.
- W2990268370 hasRelatedWork W2100335171 @default.
- W2990268370 hasRelatedWork W2106838767 @default.
- W2990268370 hasRelatedWork W2156504824 @default.
- W2990268370 hasRelatedWork W2318000084 @default.
- W2990268370 hasRelatedWork W2532549377 @default.
- W2990268370 hasRelatedWork W2743543774 @default.
- W2990268370 hasRelatedWork W2916140740 @default.
- W2990268370 hasRelatedWork W4292263289 @default.
- W2990268370 isParatext "false" @default.
- W2990268370 isRetracted "false" @default.
- W2990268370 magId "2990268370" @default.
- W2990268370 workType "article" @default.