  Effective flood hazard management requires evaluating and predicting flash
flood susceptibility. Convolutional neural networks (CNNs) are commonly used
for this task but face issues like gradient explosion and overfitting. This
study explores the use of an attention mechanism, specifically the
convolutional block attention module (CBAM), to enhance CNN models for flash
flood susceptibility in the ungauged Rheraya watershed, a flood prone region.
We used ResNet18, DenseNet121, and Xception as backbone architectures,
integrating CBAM at different locations. Our dataset included 16 conditioning
factors and 522 flash flood inventory points. Performance was evaluated using
accuracy, precision, recall, F1-score, and the area under the curve (AUC) of
the receiver operating characteristic (ROC). Results showed that CBAM
significantly improved model performance, with DenseNet121 incorporating CBAM
in each convolutional block achieving the best results (accuracy = 0.95, AUC =
0.98). Distance to river and drainage density were identified as key factors.
These findings demonstrate the effectiveness of the attention mechanism in
improving flash flood susceptibility modeling and offer valuable insights for
disaster management.
