## 1 Introduction
To improve the SAR-based flood detection and monitoring accuracy, a range of ML algorithms have been evaluated, including support vector machines (SVM) [18], random forest (RF) [19], decision trees [20], hidden Markov trees (HMTs) [21], logistic regression (LR) [22], and maximum likelihood (MLL) [23]. These models have consistently demonstrated their effectiveness in differentiating inundated areas from non-water surfaces, offering significant advantages during themonsoon season when the presence of cloud cover reduces the utility of optical satellite imagery. These models leverage environmental and hydrological predictors, including topography, soil characteristics, land use/land cover (LULC), and precipitation data, to assess flood susceptibility. By employing RF and SVM classifiers, inundated and non-inundated areas in Kerala and Assam, India, were compared, and the conclusion was made that RF, especially with VV polarization, performed better in flood extent delineation [24]. Similarly, supervised (RF, SVM, MLC) and unsupervised models were developed for urban flood detection in San Diego, combining SAR data with road closure alerts [25]. Their findings indicated that SVM and unsupervised change detection methods (Otsu algorithm, fuzzy logic, iso-clustering) delivered high accuracy. RF, CNNs, and GIS were integrated with Sentinel-1 data in Palembang to create detailed flood risk maps, successfully managing complex spatial-temporal patterns [26]. Object-based classification using RF was employed for analyzing SAR time series data, leveraging backscatter coefficients, coherence, and multi-scale segmentation for flood mapping, achieving a 96% classification accuracy [27]. Other studies reinforced the importance of integrating various datasets for improved ML performance. Gradient boosting (GB) and RF were employed to detect the extent of flooding and analyze water depth with Sentinel-1, Sentinel-2, and DEM datasets covering Mexico, with GB outperforming RF in most cases [28]. Flood susceptibility in West Bengal was assessed using six ML models, where the Stacking Ensemble approach yielded the highest predictive accuracy, followed by XGBoost and DNN, based on 18 flood-related parameters [29]. Flood mapping was improved in complex terrains using QDA, SVM, and KNN classifiers alongside HAND data, finding QDA and SVM most effective, particularly in areas with dense vegetation and urban coverage [30]. Collectively, these studies emphasize the growing reliability and adaptability of ML in flood detection and mapping across diverse landscapes and hydrological conditions. Despite increasing interest in remote sensing applications, the synergistic use of SAR imagery and ML algorithms for inundation mapping along the GBM delta coastline is still limited, with only a few emerging studies [31; 32] exploring its potential. The frequency and spatial extent of severe weather events are increasing as climate change impacts are intensifying, with the GBM delta, particularly the coastal region of Bangladesh, facing significant consequences [33]. The growing frequency and intensity of extreme weather events have exacerbated flood risks in deltaic regions, particularly in densely populated areas [34]. While Bangladesh has made considerable progress in reducing fatalities associated with tropical cyclones, the broader socio-economic impacts, including economic losses, continue to escalate [35]. Historical storm data from the Bay of Bengal reveal a bimodal distribution in cyclone frequency, with peaks typically occurring in May and November [36]. On average, the country faces a major cyclone event, characterized by inundation across 26-34% of the area, every three years [37; 38]. Analysis of past cyclonic landfalls indicates that districts such as Sattkhira, Bagerhat, Barguna, and Patuakhali have been most frequently affected over the last 137 years [39].