Enhancing flood susceptibility modelling in Canada: Integrating seasonal meteorological data, feature selection and machine learning approaches

Floods are the costliest hazard in Canada in terms of direct infrastructure damage. Flood susceptibility modelling (FSM) identifies flood hazard areas; input features are dependent on the study area and modelling methods, which affect the reliability and accuracy of FS maps. Typical features in FSM are static topographical inputs (digital elevation model, land use, wetness index, height above nearest drainage, etc.). Though meteorological variables have been included in FSM, they are often low temporal resolution (e.g. annual); seasonal meteorological variables are often not included. The 2023 Canadian National FS map was developed using machine learning (ML) ensembles, with features that include historical flood events and 30 years of climate data. This research initiates the update to the existing Canadian FS map by expanding the suite of input features used and comparing the impact of three feature selection methods (partial correlation, partial mutual information, combined neural pathway strength) on three types of ML algorithms: random forest, artificial neural network (ANN) and convoluted neural network (CNN). The expanded set of features includes geospatial indices and flood-specific meteorological data such as spring temperature, precipitation, and vapour pressure. Data from preceding seasons to specific flood events is also included. Preliminary findings from the feature selection methods show that including seasonal flood-specific meteorological data provides important information leading to better model performance. Model performances of the three algorithms were comparable. Random forest with extreme gradient boosting led to the highest model performance (AUC = 0.98, F1 = 0.94), followed by CNN (AUC = 0.0.96, F1 = 0.90). ANN ensemble with leave-one-out-cross-validation resulted in the lowest model performance (AUC = 0.91, F1 = 0.85). Results contribute to the development of an improved national FS map for Canada.