Due to the complexity of hydrological systems a single model may be
unable to capture the full range of a catchment response and
accurately predict the streamflows. A solution could be the in use
of several specialized models organized in the so-called
committees. Refining the committee approach is one of the important
topics of this study, and it is demonstrated that it allows for
increased predictive capability of models. Another topic addressed
is the prediction of hydrologic models' uncertainty. The
traditionally used Monte Carlo method is based on the past data and
cannot be directly used for estimation of model uncertainty for the
future model runs during its operation. In this thesis the
so-called MLUE (Machine Learning for Uncertainty Estimation)
approach is further explored and extended; in it the machine
learning techniques (e.g. neural networks) are used to encapsulate
the results of Monte Carlo experiments in a predictive model that
is able to estimate uncertainty for the future states of the
modelled system. Furthermore, it is demonstrated that a committee
of several predictive uncertainty models allows for an increase in
prediction accuracy. Catchments in Nepal, UK and USA are used as
case studies. In flood modelling hydrological models are typically
used in combination with hydraulic models forming a cascade, often
supported by geospatial processing. For uncertainty analysis of
flood inundation modelling of the Nzoia catchment (Kenya) SWAT
hydrological and SOBEK hydrodynamic models are integrated, and the
parametric uncertainty of the hydrological model is allowed to
propagate through the model cascade using Monte Carlo simulations,
leading to the generation of the probabilistic flood maps. Due to
the high computational complexity of these experiments, the high
performance (cluster) computing framework is designed and used.
This study refined a number of hydroinformatics techniques, thus
enhancing uncertainty-based hydrological and integrated modelling.
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