FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4376106775> ?p ?o ?g. }

• W4376106775 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> Hydrological numerical modelling is generally designed to provide predictions of uncertain quantities in a decision-support context. In the implementation of decision-support modelling, data assimilation and uncertainty quantification are often the most difficult and time-consuming tasks. This is because the imposition of history-matching constraints on model parameters usually requires a large number of model runs. Data Space Inversion (DSI) provides an alternative (and highly model-run-efficient) method for predictive uncertainty quantification that avoids the need for parameter estimation. It does this by evaluating covariances between model outputs used for history matching (e.g. hydraulic heads) and model predictions based on model runs that sample the prior parameter probability distribution. By focusing on the direct relationship between model outputs under historical conditions and predictions of system behaviour under future conditions, DSI avoids the need to estimate or adjust model parameters. This is advantageous when using such as Integrated Surface and Subsurface Hydrologic Models (ISSHMs). These models are characterised by long run times, a penchant for numerical instability and/or complex parameterisation schemes that are designed to maintain geological realism. This paper demonstrates that DSI provides a robust and efficient means of quantifying the uncertainties of complex model predictions, at the same time as it provides a basis for complementary linear analyses that can explore issues such as data worth. DSI is applied in conjunction with an ISSHM representing a synthetic but realistic stream-aquifer system. Predictions of interest are fast travel times and surface water infiltration. Linear and nonlinear estimates of prediction uncertainty based on DSI are validated against a more traditional approach to prediction uncertainty quantification which requires adjustment of a large number of parameters. A DSI-generated surrogate model is then used to investigate the effectiveness and efficiency of existing and possible future monitoring networks. This demonstrates the benefits of using DSI in conjunction with a complex numerical model to quantify prediction uncertainty and support data worth analysis in complex hydrogeological environments." @default.
• W4376106775 created "2023-05-12" @default.
• W4376106775 creator A5090768746 @default.
• W4376106775 date "2023-05-10" @default.
• W4376106775 modified "2023-10-16" @default.
• W4376106775 title "Reply on RC2" @default.
• W4376106775 doi "https://doi.org/10.5194/gmd-2023-40-ac2" @default.
• W4376106775 hasPublicationYear "2023" @default.
• W4376106775 type Work @default.
• W4376106775 citedByCount "0" @default.
• W4376106775 crossrefType "peer-review" @default.
• W4376106775 hasAuthorship W4376106775A5090768746 @default.
• W4376106775 hasBestOaLocation W43761067751 @default.
• W4376106775 hasConcept C109007969 @default.
• W4376106775 hasConcept C11413529 @default.
• W4376106775 hasConcept C119857082 @default.
• W4376106775 hasConcept C121332964 @default.
• W4376106775 hasConcept C124101348 @default.
• W4376106775 hasConcept C126255220 @default.
• W4376106775 hasConcept C127313418 @default.
• W4376106775 hasConcept C151730666 @default.
• W4376106775 hasConcept C153294291 @default.
• W4376106775 hasConcept C1893757 @default.
• W4376106775 hasConcept C24552861 @default.
• W4376106775 hasConcept C2779343474 @default.
• W4376106775 hasConcept C32230216 @default.
• W4376106775 hasConcept C33923547 @default.
• W4376106775 hasConcept C41008148 @default.
• W4376106775 hasConceptScore W4376106775C109007969 @default.
• W4376106775 hasConceptScore W4376106775C11413529 @default.
• W4376106775 hasConceptScore W4376106775C119857082 @default.
• W4376106775 hasConceptScore W4376106775C121332964 @default.
• W4376106775 hasConceptScore W4376106775C124101348 @default.
• W4376106775 hasConceptScore W4376106775C126255220 @default.
• W4376106775 hasConceptScore W4376106775C127313418 @default.
• W4376106775 hasConceptScore W4376106775C151730666 @default.
• W4376106775 hasConceptScore W4376106775C153294291 @default.
• W4376106775 hasConceptScore W4376106775C1893757 @default.
• W4376106775 hasConceptScore W4376106775C24552861 @default.
• W4376106775 hasConceptScore W4376106775C2779343474 @default.
• W4376106775 hasConceptScore W4376106775C32230216 @default.
• W4376106775 hasConceptScore W4376106775C33923547 @default.
• W4376106775 hasConceptScore W4376106775C41008148 @default.
• W4376106775 hasLocation W43761067751 @default.
• W4376106775 hasOpenAccess W4376106775 @default.
• W4376106775 hasPrimaryLocation W43761067751 @default.
• W4376106775 hasRelatedWork W1967604305 @default.
• W4376106775 hasRelatedWork W2095394046 @default.
• W4376106775 hasRelatedWork W2347219288 @default.
• W4376106775 hasRelatedWork W2366221835 @default.
• W4376106775 hasRelatedWork W2383943445 @default.
• W4376106775 hasRelatedWork W2386767533 @default.
• W4376106775 hasRelatedWork W2615521230 @default.
• W4376106775 hasRelatedWork W2781745042 @default.
• W4376106775 hasRelatedWork W3036554888 @default.
• W4376106775 hasRelatedWork W3103459669 @default.
• W4376106775 isParatext "false" @default.
• W4376106775 isRetracted "false" @default.
• W4376106775 workType "peer-review" @default.