Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

This study concentrates on: (i) committee modelling of hydrological models, (ii) hybrid committee hydrological models, (iii) influence of sampling strategies on prediction uncertainty of hydrological models, (iv) uncertainty prediction using machine learning techniques, (v) committee of predictive uncertainty models and (vi) flood inundation model and their uncertainty. This thesis is a contribution to hydroinformatics, which aims to connect various scientific disciplines: hydrological modelling, hydrodynamic modelling, multi-model averaging, data driven models, hybrid hydrological models, uncertainty analysis and high performance computing. The conclusions drawn allow for advancing the theory and practice of hydrological and integrated modelling. The developed software components is made available for public use and can be used by the researchers and practitioners to advance the mentioned areas further.