Explainable convolutional neural network for flood susceptibility mapping in Southern Ontario  
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".