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

by Abebe Andualem Jemberie

Overview

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

Product Details

ISBN-13:	9781138405578
Publisher:	Taylor & Francis
Publication date:	07/03/2017
Pages:	200
Product dimensions:	6.88(w) x 9.69(h) x (d)

Table of Contents

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