## 6 Conclusions  
This study underscores the expanding potential of SAR imagery integrated with ML algorithms in advancing flood inundation monitoring, particularly in coastal and deltaic environments exposed to compound climate hazards. Focusing on the GBM delta coastline of Bangladesh, the application of SVM, RF, and KNN classifiers demonstrated that SVM, achieving 75% accuracy, offers the most reliable performance in flood detection due to its robustness and strong discriminative capability. The analysis of monsoonal and cyclone-induced floods between 2017 and 2024 revealed that high-intensity rainfall events--particularly those exceeding 100 mm/day--triggered widespread inundation in western coastal zones, while storm surges and persistent winds from cyclones led to repeated flooding in southwestern districts. These findings carry broader implications for flood-prone deltas and low-lying coastal regions globally, which face similar compound flooding risks driven by extreme rainfall and tropical cyclones. The use of SAR imagery, unaffected by cloud cover, combined with hydro-meteorological indicators such as rainfall intensity and cyclone tracks, provides a scalable and transferable approach for detecting both immediate flood hotspots and evolving vulnerability patterns under changing climate conditions. Importantly, distinguishing between flood mechanisms--whether rainfall-driven or cyclone-induced--can guide the design of targeted early warning systems, adaptive infrastructure (e.g., raised shelters, improved drainage, embankments), and climate-resilient development plans tailored to regional hazards. Comparative analysis revealed that cyclone events affected larger areas under low-level inundation (19,085 sq. km), whereas monsoonal events resulted in more severe high-impact flooding (1626 sq. km), highlighting the need for differentiated risk management strategies.  
As climate extremes grow in frequency and intensity across the globe, we believe the methodological framework and findings of this study provide a valuable foundation for improving early warning systems, disaster preparedness, and long-term resilience in other vulnerable coastal deltas. Future research should emphasize a multi-temporal approach and utilize high-frequency satellite constellations to more effectively capture the rapid dynamics of flooding. Integrating SAR-based flood monitoring into dynamic forecasting models has the potential to significantly enhance real-time flood management and early warning capabilities worldwide--an essential step toward strengthening adaptive capacity and protecting at-risk communities in an era of a warming world.  
Conceptualization, S.; methodology, S.; analysis, N.N.N., M.M.W., S.D. and M.P.H.; validation, S.D. and H.M.M.; writing--original draft preparation, S., N.N.N., M.M.W., S.M.T.Z. and Z.T.; writing--review and editing, A.K.M.S.I. and M.A.H.; supervision, S., A.K.M.S.I. and M.A.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Global Center on Adaptation (GCA).  
The data presented in this study are available on request to the corresponding author.  
This work was supported by the project entitled, \"Enhancing Climate Resilience of Coastal Towns of Bangladesh Through Nature-Based Solutions\", carried out by the. Institute of Water and Flood Management (IWFM) of Bangladesh University of Engineering and Technology (BUET).  
The authors declare no conflicts of interest.