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Information Theory and Artificial Intelligence to Manage Uncertainty in Hydrodynamic and Hydrological Models (Paperback)
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Information Theory and Artificial Intelligence to Manage Uncertainty in Hydrodynamic and Hydrological Models (Paperback)
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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
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.
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