## 5 Discussion
As a lazy learner, KNN's instance-based approach can lead to variability across folds, especially when the optimal k-value fluctuates. RF performs comparatively poorly in both evaluations. Despite its ensemble design to reduce variance, RF's underperformance suggests the dataset may lack the complex feature interactions needed for tree-based models. The high cross-validation variability (\(\pm\)0.1670) further indicates instability, likely stemming from its reliance on random sampling and feature selection, which may not align well with the dataset's structure. However, performance varies across regions and conditions, highlighting the need for context-specific model selection. For instance, it has been reported that the RF classifier, particularly when using VV polarization, outperformed other methods in delineating flood extents in Kerala and Assam, India [24]. In contrast, SVM and unsupervised change detection techniques yielded superior accuracy in another study [25]. Collectively, these findings highlight the significance of tailoring threshold values and model choices to specific geographic and operational contexts, balancing classification performance with computational efficiency to ensure practical applicability. Despite these challenges, the proposed machine learning framework offers scalable potential for flood mapping and risk assessment in other vulnerable regions. Integrating such systems into disaster management can enable more timely and accurate flood warnings, representing a critical step toward adaptive, data-driven resilience strategies. The observed inundation patterns along the GBM delta coastline are influenced by several hydro-meteorological factors, including tidal conditions, rainfall intensity, and prevailing sea levels. For instance, despite having comparable area-averaged daily rainfall values (\(\sim\)110 mm/day), the extent of inundation varied significantly across the years 2017, 2020, and 2024 (monsoonal rainfall), resulting in flooded areas of 9639 km\({}^{2}\), 5396 km\({}^{2}\), and 9888 km\({}^{2}\), respectively (Figure 8). This variation highlights the role of additional drivers such as tidal phase. Notably, the events in 2017 and 2024 occurred during spring tides, which are typically associated with higher water levels, while the 2020 event coincided with a heap tide, possibly explaining the reduced inundation despite similar rainfall. The highest inundation for monsoonal rainfall observed during the study period occurred on 7 August 2023, despite a relatively lower daily rainfall of 90 mm. This event took place during a heap tide, yet it resulted in extensive flooding. This anomaly can be attributed to a preceding monsoon depression that crossed the Bangladesh coast between 1-3 August 2023, likely saturating the ground and initiating early inundation. The subsequent rainfall on 7 August exacerbated already critical conditions [69], suggesting that antecedent moisture and pre-event flooding play crucial roles in modulating flood extent. Findings also corroborate historical reports of coastal flooding events. For example, significant monsoon flooding in June 2019 affecting Feni, Noakhali, Khagrachari, Bandarban, Rangamati, Cox's Bazar, and Chattopram was documented by the International Federation of Red Cross and Red Crescent Societies [70]. Our analysis confirms in unokhali and Maheshkhali; however, the other affected districts listed in the IFRC (2019) [70] report primarily experienced upstream riverine flooding rather than coastal inundation. Similarly, continuous heavy rainfall was reported in July 2021, causing severe flooding in Cox's Bazar Sadar, Teknaf, Ukiuya, and Ramu upazilas [71]. Our satellite-based analysis confirms comparable inundation patterns in these regions (Figure 8e), affirming the utility of remote sensing in capturing localized flood impacts. In August 2024, a series of intense and consecutive rainfall events led to severe flooding across Noakhali, Cumilla, Laxmipur, Feni, and Chattopram [72].