Hussain Muhammad Muktadir

The Ganges–Brahmaputra–Meghna (GBM) delta, characterized by complex topography and hydrological conditions, is highly susceptible to recurrent flooding, particularly in its coastal regions where tidal dynamics hinder floodwater discharge. This study integrates Synthetic Aperture Radar (SAR) imagery with machine learning (ML) techniques to assess near real-time flood inundation patterns associated with extreme weather events, including recent cyclones between 2017 to 2024 (namely, Mora, Titli, Fani, Amphan, Yaas, Sitrang, Midhili, and Remal) as well as intense monsoonal rainfall during the same period, across a large spatial scale, to support disaster risk management efforts. Three machine learning algorithms, namely, random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN), were applied to flood extent data derived from SAR imagery to enhance flood detection accuracy. Among these, the SVM algorithm demonstrated the highest classification accuracy (75%) and exhibited superior robustness in delineating flood-affected areas. The analysis reveals that both cyclone intensity and rainfall magnitude significantly influence flood extent, with the western coastal zone (e.g., Morrelganj and Kaliganj) being most consistently affected. The peak inundation extent was observed during the 2023 monsoon (10,333 sq. km), while interannual variability in rainfall intensity directly influenced the spatial extent of flood-affected zones. In parallel, eight major cyclones, including Amphan (2020) and Remal (2024), triggered substantial flooding, with the most severe inundation recorded during Cyclone Remal with an area of 9243 sq. km. Morrelganj and Chakaria were consistently identified as flood hotspots during both monsoonal and cyclonic events. Comparative analysis indicates that cyclones result in larger areas with low-level inundation (19,085 sq. km) compared to monsoons (13,829 sq. km). However, monsoon events result in a larger area impacted by frequent inundation, underscoring the critical role of rainfall intensity. These findings underscore the utility of SAR-ML integration in operational flood monitoring and highlight the urgent need for localized, event-specific flood risk management strategies to enhance flood resilience in the GBM delta.

Sandbars are an integral part of the alluvial river’s geophysical system due to these rivers’ wide sediment availability and varied transport capacity. The sandbars’ evolution and translation considerably influence the stability of the riverbank. However, while designing the riverbank protection structures (RBPS), the impact of such sandbars is often overlooked, as the evolution of such bars is quite uncertain in terms of location, amplitude, and translation. This study evaluates the localized impact of sandbars on bank protection structures in two types of alluvial rivers: meandering (Ganges) and braided (Brahmaputra-Jamuna), utilizing time series satellite images, hydraulic characteristics, and numerical modeling. We found that sandbar development initiates width adjustment in both meandering and braided rivers when the ratio of width to depth surpasses 90. In the case of meandering rivers, riverbank erosion mostly occurs as a result of the presence of alternate bars or point bars. Sandbars in a meandering river (Ganges) can lead to an approximate 18% increase in flow depth. The depth-averaged velocity is anticipated to rise by approximately 29%, and the tractive force may increase by a factor of 1.6. On the other hand, the braided river (the Brahmaputra-Jamuna) underwent significant bank erosion due to the presence of both free unit and hybrid types of bars. In such rivers, the depth of the flow may experience a notable increase of 18%, while the depth-averaged velocity undergoes an approximate increase of 50%, and the tractive force has the potential to grow by a factor of 5.3. Consequently, we recommend allowing the natural evolution of sandbars while preserving the riverbank (where needed only) through RBPS, considering these additional loads.