River ice breakup is a vital process to the morphology and hydrology of many rivers in Canada, often governing peak flows of the river. These events can occur through multiple mechanisms, with the potential for volatile or early breakup events that can have severe impacts to the river. Ice jam flooding can be a potentially devastating result of river ice breakup while early breakup of ice cover in a mid-winter breakup can be unpredictable and greatly alter the remaining ice season. These events are growing increasingly common as a result of climate change, and as a result there is a need to develop prediction tools for these events to aid in decision making support. Past investigations into developing such tools, especially from a data-driven modelling perspective, are challenged by the availability and complexity of the data related to these rare and dangerous to measure events. Therefore, the goal of this dissertation was to develop and apply methods to address the historical challenges and shortcomings in predicting these events through the use of data-driven modelling techniques. This includes: i) development of a stacking ensemble modelling framework for the prediction of ice jam presence during the spring breakup season of a river, utilising variable selection and rare-event forecasting techniques in combination with a comprehensive selection of machine-learning algorithms; ii) return period and trend analysis of mid-winter breakups in conjunction with comprehensive input analysis techniques to identify the key drivers of these events’ severity and develop a means of classifying the flood risk based on hydroclimatic traits; iii) the development of a two-level modelling system for the prediction of the occurrence and timing of mid-winter breakups on a national scale utilising rare event forecasting techniques and imbalanced learning; and iv) development of a novel hybrid semantic and machine learning modelling system in which an ontology is used in conjunction with network analysis techniques to select variables for machine learning models, which is used on a national case study of the prediction of spring breakup timing in Canada. The results of each study in application to their respective case studies demonstrate the effectiveness of the proposed techniques, which are shown to be easily adaptable to other regions or locations. These techniques can form the backbone of decision-making support for communities on rivers that are affected by the unpredictable and oftentimes volatile nature of river ice breakup. / Thesis / Candidate in Philosophy / River ice breakup is a key event to the hydrology of rivers throughout Canada, playing a major role in their physical and ecological characteristics. The timing and mechanism of these events can, however, be unpredictable and volatile, with the effects of climate change only exacerbating these risks. This dissertation focuses on addressing these potential issues through the application of machine learning and hybrid modeling in the prediction of river ice breakup events. Advanced data driven techniques coupled with novel applications of other analytical methods are used to: i) predict the presence of ice jams through the application of stacking ensemble modelling; ii) predict the severity of mid-winter breakups through application of trend and variable analysis; iii) predict the occurrence and timing of mid-winter breakups using rare-event forecasting techniques; and iv) develop a novel hybrid modelling scheme coupling ontology-based semantic modelling and machine learning to predict spring breakup timing. Detailed case studies for each application are provided demonstrating the effectiveness of the discussed techniques.