## 1 Introduction  
Owing to its unique topography, hydrological setting, and geographic location, the Ganges-Brahmaputra-Meghma (GBM) delta has long been highly susceptible to flooding. Particularly in the coastal regions, hydro-geomorphological vulnerabilities render the area prone to large-scale inundation, while tidal fluctuations in the downstream sections of the delta impede the efficient downstream flow of floodwaters [1]. Flooding in the coastal belt is primarily driven by the delta's flat terrain, intense rainfall over the three upstream basins [2], and the recurrent occurrence of cyclonic storm surges [3]. The coastal zone of the GBM delta experiences both cyclone-induced storm surge flooding and monsoonal flooding, although the simultaneous occurrence of these events is rare [4]. When cyclones make landfall, the combined effects of intense rainfall and associated storm surges result in widespread flooding. The pre-monsoon period from April to June and the post-monsoon period spanning between October and November are typically when cyclone-induced inundation occurs [5], whereas monsoonal rains during July-September often overwhelm drainage systems, leading to prolonged waterlogging in low-lying areas. With projections indicating a rise in both the cyclonic surges and sea levels, the scale and impact of inundation across the deltaic coastline are likely to worsen in the coming decades [6]. Flooding remains the most widespread natural hazard in the region, causing significant human casualties and economic losses. Consequently, the development of effective flood warning and disaster response systems has become increasingly urgent [7]. Near real-time flood monitoring and inundation mapping are critical for a timely response during such events [8]. However, implementing reliable and timely flood monitoring systems remains challenging due to the need for high-quality, real-time data [9]. In this context, remote sensing technology has been gaining prominence since the 1990s as an essential tool for the detection and surveillance of flood-prone areas [10]. Both optical and microwave satellite data have been extensively used in monitoring floods and mapping areas of inundation [11]. However, optical sensors are less reliable under cloud-heavy conditions, which commonly accompany monsoons and cyclones [12]. Synthetic Aperture Radar (SAR), in contrast, offers a powerful alternative due to its all-weather imaging capability. SAR has proven effective in global flood mapping efforts for its ability to penetrate clouds and accurately differentiate between water and non-water surfaces [13]. Its use of lower microwave frequencies enhances this capacity by improving the contrast between inundated and non-inundated areas, making SAR a preferred tool for assessing floods in near real time [14,15]. However, SAR imagery comes with certain limitations, such as the presence of speckle noise, which results from the interference of radar signals reflected by multiple small scatterers [16,17]. Differentiating between standing water, saturated soil, and smooth surfaces such as asphalt can also pose challenges. Despite these constraints, SAR imagery remains a valuable tool for operational flood mapping, especially when combined with ancillary data and machine learning (ML) techniques to improve classification accuracy and reduce uncertainty. Moreover, most SAR-derived inundation mapping approaches have not been extensively validated over large geographic areas [17]. Consequently, optimizing flood and non-flood classification thresholds based on backscatter values is critical for improving mapping accuracy. Recent advancements in flood detection and mapping have seen extensive use of ML integrated with remote sensing data, particularly SAR imagery.