Information Theory and Artificial Intelligence to Manage Uncertainty in Hydrodynamic and Hydrological Models (1)
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- Synopsis
- 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.
- Copyright:
- 2004
Book Details
- Book Quality:
- Publisher Quality
- Book Size:
- 184 Pages
- ISBN-13:
- 9781040215630
- Related ISBNs:
- 9781138405578, 9781482284034, 9789058096951, 9780429179402
- Publisher:
- CRC Press
- Date of Addition:
- 01/29/25
- Copyrighted By:
- Taylor & Francis Group pic, London, UK
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Science, Technology
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.