FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2610366230> ?p ?o ?g. }

• W2610366230 endingPage "1741" @default.
• W2610366230 startingPage "1723" @default.
• W2610366230 abstract "Data assimilation is widely used to improve flood forecasting capability, especially through parameter inference requiring statistical information on the uncertain input parameters (upstream discharge, friction coefficient) as well as on the variability of the water level and its sensitivity with respect to the inputs. For particle filter or ensemble Kalman filter, stochastically estimating probability density function and covariance matrices from a Monte Carlo random sampling requires a large ensemble of model evaluations, limiting their use in real-time application. To tackle this issue, fast surrogate models based on polynomial chaos and Gaussian process can be used to represent the spatially distributed water level in place of solving the shallow water equations. This study investigates the use of these surrogates to estimate probability density functions and covariance matrices at a reduced computational cost and without the loss of accuracy, in the perspective of ensemble-based data assimilation. This study focuses on 1-D steady state flow simulated with MASCARET over the Garonne River (South-West France). Results show that both surrogates feature similar performance to the Monte-Carlo random sampling, but for a much smaller computational budget; a few MASCARET simulations (on the order of 10–100) are sufficient to accurately retrieve covariance matrices and probability density functions all along the river, even where the flow dynamic is more complex due to heterogeneous bathymetry. This paves the way for the design of surrogate strategies suitable for representing unsteady open-channel flows in data assimilation." @default.
• W2610366230 created "2017-05-12" @default.
• W2610366230 creator A5000800385 @default.
• W2610366230 creator A5019560085 @default.
• W2610366230 creator A5032851998 @default.
• W2610366230 creator A5061973742 @default.
• W2610366230 creator A5062372346 @default.
• W2610366230 creator A5064586602 @default.
• W2610366230 creator A5065614381 @default.
• W2610366230 date "2017-10-23" @default.
• W2610366230 modified "2023-10-15" @default.
• W2610366230 title "Comparison of polynomial chaos and Gaussian process surrogates for uncertainty quantification and correlation estimation of spatially distributed open-channel steady flows" @default.
• W2610366230 cites W100464641 @default.
• W2610366230 cites W105972687 @default.
• W2610366230 cites W1504847839 @default.
• W2610366230 cites W1542323827 @default.
• W2610366230 cites W1633869374 @default.
• W2610366230 cites W171679735 @default.
• W2610366230 cites W1919033476 @default.
• W2610366230 cites W1980921479 @default.
• W2610366230 cites W1986837469 @default.
• W2610366230 cites W1993286134 @default.
• W2610366230 cites W1996475268 @default.
• W2610366230 cites W2008034179 @default.
• W2610366230 cites W2009675688 @default.
• W2610366230 cites W2017486265 @default.
• W2610366230 cites W2018159038 @default.
• W2610366230 cites W2019797235 @default.
• W2610366230 cites W2032291632 @default.
• W2610366230 cites W2036242000 @default.
• W2610366230 cites W2036843121 @default.
• W2610366230 cites W2049774453 @default.
• W2610366230 cites W2065775831 @default.
• W2610366230 cites W2077821963 @default.
• W2610366230 cites W2088880006 @default.
• W2610366230 cites W2091819605 @default.
• W2610366230 cites W2097441841 @default.
• W2610366230 cites W2108604227 @default.
• W2610366230 cites W2112266903 @default.
• W2610366230 cites W2113117406 @default.
• W2610366230 cites W2120731138 @default.
• W2610366230 cites W2120933360 @default.
• W2610366230 cites W2122801340 @default.
• W2610366230 cites W2126715275 @default.
• W2610366230 cites W2147746661 @default.
• W2610366230 cites W2153576883 @default.
• W2610366230 cites W2156069580 @default.
• W2610366230 cites W2169163463 @default.
• W2610366230 cites W2183264249 @default.
• W2610366230 cites W2286018235 @default.
• W2610366230 cites W2310768323 @default.
• W2610366230 cites W2315089058 @default.
• W2610366230 cites W2342500502 @default.
• W2610366230 cites W2468919567 @default.
• W2610366230 cites W2765449855 @default.
• W2610366230 cites W2787894218 @default.
• W2610366230 cites W2963032837 @default.
• W2610366230 cites W2963889731 @default.
• W2610366230 cites W3105621768 @default.
• W2610366230 cites W4231397577 @default.
• W2610366230 cites W4238717354 @default.
• W2610366230 cites W4239167255 @default.
• W2610366230 cites W4242682838 @default.
• W2610366230 cites W4300600608 @default.
• W2610366230 cites W601879004 @default.
• W2610366230 doi "https://doi.org/10.1007/s00477-017-1470-4" @default.
• W2610366230 hasPublicationYear "2017" @default.
• W2610366230 type Work @default.
• W2610366230 sameAs 2610366230 @default.
• W2610366230 citedByCount "22" @default.
• W2610366230 countsByYear W26103662302018 @default.
• W2610366230 countsByYear W26103662302019 @default.
• W2610366230 countsByYear W26103662302020 @default.
• W2610366230 countsByYear W26103662302021 @default.
• W2610366230 countsByYear W26103662302022 @default.
• W2610366230 countsByYear W26103662302023 @default.
• W2610366230 crossrefType "journal-article" @default.
• W2610366230 hasAuthorship W2610366230A5000800385 @default.
• W2610366230 hasAuthorship W2610366230A5019560085 @default.
• W2610366230 hasAuthorship W2610366230A5032851998 @default.
• W2610366230 hasAuthorship W2610366230A5061973742 @default.
• W2610366230 hasAuthorship W2610366230A5062372346 @default.
• W2610366230 hasAuthorship W2610366230A5064586602 @default.
• W2610366230 hasAuthorship W2610366230A5065614381 @default.
• W2610366230 hasBestOaLocation W26103662302 @default.
• W2610366230 hasConcept C105795698 @default.
• W2610366230 hasConcept C11413529 @default.
• W2610366230 hasConcept C121332964 @default.
• W2610366230 hasConcept C126255220 @default.
• W2610366230 hasConcept C137250428 @default.
• W2610366230 hasConcept C153294291 @default.
• W2610366230 hasConcept C157286648 @default.
• W2610366230 hasConcept C163716315 @default.
• W2610366230 hasConcept C178650346 @default.
• W2610366230 hasConcept C185142706 @default.
• W2610366230 hasConcept C19499675 @default.
• W2610366230 hasConcept C197055811 @default.
• W2610366230 hasConcept C197656079 @default.

• next