DEVELOPMENT OF HYBRID APPROACHES FOR UNCERTAINTY QUANTIFICATION IN HYDROLOGICAL MODELING

Abstract

Water is a scarce resource especially as the water demand is significantly increasing due to the rapid growth of population. Hydrological modelling has gained a lot of attention, as it is the key to predict water availability, optimize the use of water resources and develop risk mitigation schemes. There are still many challenges in hydrological modelling that researchers and designers are trying to solve. These challenges include, but not limited to: i) there is no single robust model that can perform well in all watersheds; ii) model parameters are often associated with uncertainty, which makes the results inconclusive; iii) the required computational power for uncertainty quantification increases with the increase in model complexity; iv) some modelling assumptions to simplify computational complexity, such as parameter independence are, are often not realistic. These challenges make it difficult to provide robust hydrological predictions and/or to quantify the uncertainties within hydrological models in an efficient and accurate way. This study aims to provide more robust hydrological predictions by developing a set of hybrid approaches. Firstly, a hybrid hydrological data-driven (HHDD) model based on the integration of a physically-based hydrological model (HYMOD) and a data-driven model (artificial neural network, ANN) is developed. The HHDD model is capable of improving prediction accuracy and generating interval flow prediction results. Secondly, a hybrid probabilistic forecasting approach is developed by linking the polynomial chaos expansion (PCE) method with ANN. The results indicate that PCE-ANN can be as reliable as but much more efficient than the traditional Monte-Carlo (MC) method for probabilistic flow forecasting. Finally, a hybrid uncertainty quantification approach that can address parameter dependence is developed through the integration of principal component analysis (PCA) with PCE. The results from this dissertation research can provide valuable technical and decision support for hydrological modeling and water resources management under uncertainty.