Information Theory and Artificial Intelligence to Manage Uncertainty in Hydrodynamic and Hydrological Models e-bog
802,25 DKK
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The complementary nature of physically-based and data-driven models in their demand for physical insight and historical data, leads to the notion that the predictions of a physically-based model can be improved and the associated uncertainty can be systematically reduced through the conjunctive use of a data-driven model of the residuals. The objective of this thesis is to minimise the inevita...
E-bog
802,25 DKK
Forlag
CRC Press
Udgivet
21 april 2014
Længde
184 sider
Genrer
TNF
Sprog
English
Format
pdf
Beskyttelse
LCP
ISBN
9781482284034
The complementary nature of physically-based and data-driven models in their demand for physical insight and historical data, leads to the notion that the predictions of a physically-based model can be improved and the associated uncertainty can be systematically reduced through the conjunctive use of a data-driven model of the residuals. The objective of this thesis is to minimise the inevitable mismatch between physically-based models and the actual processes as described by the mismatch between predictions and observations. The complementary modelling approach is applied to various hydrodynamic and hydrological models.