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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 inevitable mismatch between physically-based models and the actual processes as described by the mismatch between predictions and ...

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    eBook icon PDF eBook Information Theory and Artificial Intelligence to Manage Uncertainty in Hydrodynamic and Hydrological Models

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    • Title: Information Theory and Artificial Intelligence to Manage Uncertainty in Hydrodynamic and Hydrological Models by Abebe Andualem Jemberie
    • Publisher: Taylor & Francis
    • Print ISBN: 9781138405578, 1138405574
    • eText ISBN: 9781482284034
    • Edition: 2017 1st edition
    • Format: PDF eBook
    $63.25
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