Explainable convolutional neural network for flood susceptibility mapping in Southern Ontario

Flood susceptibility mapping (FSM) plays a crucial role in proactive flood risk management, particularly in light of increasing fluvial flooding events. Traditional FSM methods, such as physics-based and qualitative approaches, are hindered by either high computational demands or inherent uncertainty. To address this, machine learning (ML) models have become an increasingly popular FSM approach, though commonly cited as black-box approaches due to the difficulty associated with understanding their underlying mechanisms. In order to better understand the ML approaches used for FSM, this study uses the gradient-weighted class activation mapping (Grad-CAM) to interpret flood susceptibility predictions of a convolutional neural network (CNN) for the Don River watershed in Ontario, Canada. Grad-CAM is an explainable algorithm highlighting input regions that are influential to the output, aiding the user in understanding and visualizing model selected important features used to arrive at the prediction. Grad-CAM results are compared to the commonly used shapley additive explanation (SHAP) algorithm. SHAP is used to calculate the relative contribution of each input onto the output, and provides a benchmark for comparisons due to its popularity.

A two dimensional CNN with an architecture of two convolutional layers, two pooling layers and a fully connected layer is used to predict flood susceptibility. The inputs to the CNN include topographical and climactic variables across the entire watershed, with a 60-40% training and testing split respectively. The results of the CNN were compared against the floodplain map of the Don River. Using the area under curve- receiver operating characteristics (AUC-ROC) as a performance metric, the CNN exhibits high performance with an AUC-ROC of 0.96.

The study highlights the potential of CNNs for flood susceptibility mapping, as well as compares two explainable machine learning algorithms, helping to further their application within FSM. Explainable algorithms are essential to decision makers in flood risk management for proactive planning and resource allocation. Future work should explore expanding the scope to predict flood susceptibility at a nationwide level.