A methodology for deriving extreme nearshore sea conditions for structural design and flood risk analysis
Coastal Engineering, ISSN: 0378-3839, Vol: 88, Page: 15-26
2014
- 92Citations
- 131Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Extreme sea conditions in the nearshore zone are required for coastal flood risk analysis and structural design. Many multivariate extreme value methods that have been applied in the past have been limited by assumptions relating to the dependence structure in the extremes. A conditional extremes statistical model overcomes a number of these previous limitations. To apply the method in practice, a Monte Carlo sampling procedure is required whereby large samples of synthetically generated events are simulated. The use of Monte Carlo approaches, in combination with computationally intensive physical process models, can raise significant practical challenges in terms of computation. To overcome these challenges there has been extensive research into the use of meta-models. Meta-models are approximations of computationally intensive physical process models (simulators). They are derived by fitting functions to the outputs from simulators. Due to their simplified representation they are computationally more efficient than the simulators they approximate.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0378383914000210; http://dx.doi.org/10.1016/j.coastaleng.2014.01.012; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84894030540&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0378383914000210; https://dx.doi.org/10.1016/j.coastaleng.2014.01.012
Elsevier BV
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