On November 15, 2007, Bangladesh was hit by the category 4 storm Severe Cyclonic Storm Sidr. At least 3,447 deaths were reported in coastal areas of Bangladesh, mainly owing to the storm surge, which was >5 m. A study team of several faculty members of Bangladesh University of Engineering and Technology (BUET) was formed to visit areas affected by Sidr, two months after the storm. It was found that cyclone shelter capacities were inadequate for the population of the service area. Without shelters, many livestock animals died during the disaster. Many people neglected cyclone warnings, which resulted in many deaths. To improve cyclone shelter performance, this study makes many recommendations, including building more cyclone shelters, providing shelters for livestock, and improvement of warning systems.

The impact of cyclone track features (e.g., cyclone translation speed, cyclone path and cyclone landfall crossing angle) in combination with tidal phase shift upon surge characteristics have been investigated at the Bay of Bengal along the Bangladesh coast. A two-dimensional hydrodynamic model in a horizontal direction (2DH) coupled with a storm-surge model has been employed for the study. Numerical experiments with three different cyclone translation speeds show that when the surge height is directly forced by the cyclonic wind speed especially within the RWM (Radius of Maximum Wind), faster translation speed produces reduced surge height as the cyclone gets less time to force the water. On the other hand, at locations outside the RMW, surge waves travel as a propagating long wave where higher surges are produced by faster moving cyclones. It is found that surge arrival times are more and more affected by tidal phase when cyclone translation speed is reduced. Analysis of seven hypothetical parallel cyclone paths show that local bathymetry and complex coastline configurations strongly influence the surge height and surge arrival time along the Bangladesh coast. From the analyses of cyclone landfall crossing angles at the Khulna and Chittagong coasts, it is observed that surge durations are the smallest at both the coasts when the coastline crossing angles are the smallest.

Impact of storm surge largely varies depending on the tidal phase during the landfall of a tropical cyclone. This study investigates comparative variance in inundation condition and thrust force for an identical cyclone during low tide and high tide by applying a numerical model (Delft3D) and a semi-analytical model (DFM). A moderate strength cyclone, Mora, which made landfall on Bangladesh coast in May 2017 is selected to study its impact on land during low tide and high tide. Actual landfall time of Mora was during low tide. To study the impact of storm surge during high tide, a synthetic cyclone is created which has similar strength and track to that of Mora but makes landfall during high tide. The results show that inundation depth, inundation extent, and thrust force increase when a cyclone makes landfall during high tide compared to the condition when the cyclone makes landfall during low tide. But the relation between storm surge impact and tidal phase is not linear. It depends on the land topography of the location, direction of cyclone movement, direction and magnitude of water velocity and wind velocity, gradients of water surface and wind velocity, and proximity and position of the location with respect to cyclone track.

CdSe polycrystalline films have been prepared on ultra-clean glass substrates by a screen-printing technique and then annealed in air. The films prepared by this technique are comparatively thicker than those prepared by other, more sophisticated techniques and optimum conditions for preparing good quality screen-printed films have been found. The optical band gaps (E _g) of the films were determined by UV transmission spectroscopy. The zinc-blende structure of the films was confirmed by X-ray diffraction analysis. The Schottky barrier height and the ideality factor for Al/CdSe junctions were determined from current–voltage characteristics. The DC conductivity of the films was measured in vacuum by a two-probe technique.

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.