Uncertainty analysis in rainfall-runoff modelling : application of machine learning techniques; Dissertation, UNESCO-IHE Institute for Water Education, Delft.

This thesis presents powerful machine learning (ML) techniques to build predictive models of uncertainty with application to hydrological models. Two different methods are developed and tested. First one focuses on parameter uncertainty analysis by emulating the results of Monte Carlo simulations of hydrological models using efficient ML techniques. Second method aims at modelling uncertainty by building an ensemble of specialised ML models on the basis of past hydrological model’s performance. Methods employed include artificial neural networks, model trees, locally weighted regression and fuzzy logic. The application of the methods to several real-world case studies demonstrates the capacity of machine learning techniques for building accurate and efficient predictive models of uncertainty