Multi-Event Machine Learning for Annual Flood Susceptibility Prediction across Canada

Abstract. Machine learning for flood susceptibility mapping (FSM) has traditionally relied on narrowly scoped events and temporally constrained datasets, limiting the generalizability and long-term utility of predictive models. We present a multi-event, multi-temporal modelling framework that leverages discrete flood occurrences from 2005 to 2023 to train a unified model capable of inference across an extended temporal horizon. Each flood event is treated as a spatio-temporal marker, enabling the model to learn evolving driver–event relationships and underlying temporal trends. Dynamic inputs (e.g., climate data, land use/land cover) are integrated with static geophysical features (e.g., digital terrain model and derivatives) to capture both transient and persistent influences on flood susceptibility. An XGBoost model is trained, tested, and validated using a 70/15/15 split, achieving an overall accuracy of 0.945, with true positive and true negative rates of 0.95 and 0.94, respectively. Precision scores for wet (flood-prone) and dry (non-flood-prone) classes are 0.94 and 0.95. Generated yearly national FSM maps from 2000 to 2023 were evaluated against published flood event datasets. Validation using national flood records, climate variability bulletins, and spatio-temporal analyses of year-to-year raster correlations confirms that years with elevated predicted susceptibility correspond to observed flood events. In addition, a weighted wetness score identified the years with both widespread and extreme flood-prone conditions, highlighting the model’s ability to capture multi-scale temporal dynamics. These results demonstrate that multi-event, multi-temporal modelling enhances the temporal reach and robustness of geospatial flood prediction, providing a foundation for long-term monitoring, trend analysis, and policy-relevant scenario planning.

© His Majesty the King in Right of Canada, as represented by the Minister of Natural Resources, 2026