Information Theory and Artificial Intelligence to Manage Uncertainty in Hydrodynamic and Hydrological Models - Prijzen

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...

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