# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 5 Conclusions  
This study successfully used a new hybrid deep-learning model for flood detection using Synthetic Aperture Radar (SAR) imagery, combining the latest advanced techniques, including CNN, U-Net, and Transformer. The primary aim was to evaluate the accuracy and suitability of these models in recognizing flood inundation areas under challenging conditions. The proposed model, by leveraging the VH Sentinel-1 image data, showed excellent performance in flood detection as compared with several benchmark models. In particular, Transformer and hybrid models achieved better performance compared to the U-Net and CNN (2D or 3D) models. The models also achieved comparable results on a test dataset (S1 GFloods), further indicating the high performance in detecting flood zones in SAR images.  
Integration of these models in operational flood monitoring systems would substantially advance the detection of floods in terms of speed and accuracy by providing instant and timely information for disaster management. These models, by utilizing near-real-time imagery data, can support disaster response efforts, especially when on-ground information is scarce. The results of this study demonstrate the importance of SAR data for advanced deep-learning-based flood response and preparedness, particularly with respect to such flood-prone areas where traditional infrastructure for flood detection is limited.  
Nevertheless, several issues remain to be resolved. To further improve model performance, hyperparameter fine-tuning and architectural refinement for flood detection can be potential directions for future exploration. Furthermore, integrating SAR data with other remote-sensing techniques can produce improved flood detection, especially in poor environmental conditions. The integration of such models into operational flood  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **\# Layers** & **\# Parameters** & **Trainable Parameters** & **Model Size (MB)** & **Inference Time** & **Batch Time** \\ \hline CNN 3D-2 Layers & 5 & 9618 & 9618 & 0.06 & 0.0183 & 501.54 \\ \hline CNN-Transformer & 29 & 407,683 & 407,683 & 1.64 & 0.0231 & 632.13 \\ \hline Hybrid CNN & 9 & 4130 & 4130 & 0.04 & 0.02 & 545.8 \\ \hline U-Net & 69 & 237,946 & 236,474 & 1.12 & 0.0233 & 638.21 \\ \hline CCT-U-ViT & 95 & 669,482 & 668,010 & 2.82 & 0.0258 & 705.91 \\ \hline ViT & 47 & 372,770 & 372,770 & 1.57 & 0.0383 & 1047.76 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._monitoring systems would be key for the evaluation of their performance in dynamic, real-time applications.  
Conceptualization, A.M.N.; methodology, A.N.A.-H.; software, A.M.N.; validation, A.R.T.Z.; formal analysis, A.M.N.; Investigation, A.R.T.Z.; Data curation, A.N.A.-H.; writing--original draft, A.M.N.; writing--review and editing, A.R.T.Z.; supervision, A.R.T.Z. and A.N.A.-H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.