This book presents the investigation of possibilities and different
architectures of integrating hydrological knowledge and conceptual
models with data-driven models for the purpose of hydrological flow
forecasting. Models resulting from such integration are referred to
as hybrid models. The book addresses the following specific topics:
A classification of different hybrid modelling approaches in the
context of flow forecasting.The methodological development and
application of modular models based on clustering and baseflow
empirical formulations.The integration of hydrological conceptual
models with neural network error corrector models and the use of
committee models for daily streamflow forecasting.The application
of modular modelling and fuzzy committee models to the problem of
downscaling weather information for hydrological forecasting. The
results of this research show the increased forecasting accuracy
when modular models, which integrate conceptual and data-driven
models, are considered. Committee machine modelling show to be able
to manage increased lead time with an acceptable accuracy.
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