Attention is all you need for an improved CNN-based flash flood susceptibility modeling. The case of the ungauged Rheraya watershed, Morocco

TL;DR

This paper enhances CNN-based flash flood susceptibility models by integrating an attention mechanism (CBAM), significantly improving prediction accuracy in the ungauged Rheraya watershed, with DenseNet121 plus CBAM achieving the best results.

Contribution

It introduces the use of CBAM attention modules within CNN architectures for flood susceptibility modeling, demonstrating improved performance over traditional CNNs.

Findings

CBAM significantly improves CNN model performance.

DenseNet121 with CBAM achieves 95% accuracy and 0.98 AUC.

Distance to river and drainage density are key factors.

Abstract

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.