A Residual Neural Network Integrated with a Hydrological Model for Global Flood Susceptibility Mapping Based on Remote Sensing Datasets

Identifying floods and flood susceptibility mapping are critical for decision-makers and disaster management. Machine learning and deep learning have emerged as powerful tools for flood prevention, whereas they confront the drawbacks of overfitting and biased prediction due to the difficulty in obtaining real data. Therefore, this study presents a novel approach for flood susceptibility prediction by integrating ResNet-18 with a 2D hydrological model for global flood susceptibility mapping using remote sensing datasets. The three main contributions of this study are outlined below. First, a new perspective integrating hydrological simulation and deep learning is presented to overcome the inherent drawbacks of deep learning. Second, the model performance is improved through physics-based initialization. Third, the pretrained model achieves better performance than the original model with incomplete training labels. This experiment demonstrates that the physics-based initialized ResNet-18 model achieves satisfactory prediction performance in terms of accuracy and area under the receiver operating characteristic (ROC) curve (0.854 and 0.932, respectively) and is extremely robust according to a sensitivity analysis.

Automated summary

This study presents a novel approach by integrating ResNet-18 with a 2D hydrological model for global flood susceptibility mapping. The model improves its performance through physics-based initialization and achieves better performance than the original model with incomplete training labels. The experiment demonstrates that the physics-based initialized ResNet-18 model achieves satisfactory prediction performance and is extremely robust.