This book describes the use of machine learning techniques to build
predictive models of uncertainty with application to hydrological
models, focusing mainly on the development and testing of two
different models. The first focuses on parameter uncertainty
analysis by emulating the results of Monte Carlo simulation of
hydrological models using efficient machine learning techniques.
The second method aims at modelling uncertainty by building an
ensemble of specialized machine learning models on the basis of
past hydrological model's performance. The book then demonstrates
the capacity of machine learning techniques for building accurate
and efficient predictive models of uncertainty.
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