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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Information Theory and Artificial Intelligence to Manage Uncertainty in Hydrodynamic and Hydrological Models

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

Jemberie, Abebe Andualem

Hardcover

Environmental StudiesProgramming

ISBN10: 1138405574
ISBN13: 9781138405578
Publisher: CRC Press
Published: Jul 3 2017
Pages: 200
Weight: 1.15
Height: 0.50 Width: 6.69 Depth: 9.61
Language: English
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. Principles based on information theory are used to detect the presence and nature of residual information in model errors that might help to develop a data-driven model of the residuals by treating the gap between the process and its (physically-based) model as a separate process. The complementary modelling approach is applied to various hydrodynamic and hydrological models to forecast the expected errors and accuracy, using neural

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