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

1. Auflage 2004
ISBN: 978-90-5809-695-1
Verlag: A A Balkema Publishers

Buch, Englisch, 198 Seiten, Format (B × H): 174 mm x 246 mm, Gewicht: 386 g

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 network and fuzzy rule-based models. Complementary modelling offers the opportunity of incorporating processes and data that are not considered by the model, without affecting the routine operation of physically-based models. The possibility that information may be obtained which will help to improve the physically-based model is also demonstrated.

Autoren/Hrsg.

Jemberie, Abebe Andualem

Fachgebiete

• Technische Wissenschaften Bauingenieurwesen Wasserbau
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Informationstheorie, Kodierungstheorie
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Informationstheorie, Kodierungstheorie

Inhaltsverzeichnis

Part I: Overview; Chapter 1: Introduction; Chapter 2: Background; Part II: Methodology; Chapter 3: Information Theory-Based Approaches; Chapter 4: Artificial Intelligent Approaches; Chapter 5: Complementary Modelling; Part III: Application; Chapter 6: Flow Forecasting on the Rhine and Meuse Rivers; Chapter 7: Forecasting the Accuracy of Numerical Surge Forecasts Along the Dutch Coast; Part IV: Evaluation; Chapter 8: Conclusions, Discussion and Future Work

Über Autor(innen)

Abebe Andualem Jemberie