The occurrence of heavy rainfall in the south-eastern hilly region of Bangladesh makes this area highly susceptible to recurrent flash flooding. As the region is the commercial capital of Bangladesh, these flash floods pose a significant threat to the national economy. Predicting this type of flooding is a complex task which requires a detailed understanding of the river basin characteristics. This study evaluated the susceptibility of the region to flash floods emanating from within the Karnaphuli and Sangu river basins. Twenty-two morphometric parameters were used. The occurrence and impact of flash floods within these basins is mainly associated with the volume of runoff, runoff velocity, and the surface infiltration capacity of the various watersheds. Analysis showed that major parts of the basin were susceptible to flash flooding events of a 'moderate' to 'very high' level of severity. The degree of susceptibility of ten of the watersheds was rated as 'high', and one was 'very high'. The flash flood susceptibility map drawn from the analysis was used at the sub-district level to identify populated areas at risk. More than 80% of the total area of the 16 sub-districts were determined to have a 'high' to 'very high' level flood susceptibility. The analysis noted that around 3.4 million people reside in flash flood prone areas, therefore indicating the potential for loss of life and property. The study identified significant flash flood potential zones within a region of national importance, and exposure of the population to these events. Detailed analysis and display of flash flood susceptibility data at the subdistrict level can enable the relevant organizations to improve watershed management practices and, as a consequence, alleviate future flood risk.

Introduction Rapid urbanization and associated increase in population are resulting in a higher growth of motorized traffic flow in urban areas. As a consequence, cities are experiencing different problems such as air pollution, road accidents, and congestions. In response to these problems, public transportation (PT) could help to reduce air pollution, road congestion and travel time, and dependency on non-renewable energy, which benefit both

This paper explores the impact of COVID-19 on shopping behavior in two neighboring developing economies: Bangladesh and India. While the previous studies investigating the impact of COVID-19 on shopping behavior have relied on Revealed Preference (RP) data, this paper combines RP and Stated Preference (SP) data to develop joint RP-SP discrete choice models. This makes it possible to quantify the relative impact of the situational contexts on the choice of shopping modes of households and to capture the associated heterogeneity arising from the characteristics of the households. Further, comparison of the data and the estimated model parameters of the two countries with substantial socio-cultural similarities provide insights about how differences in the state of e-commerce can lead to different levels of inertia in continuing the pre-COVID behavior. The results will be useful to planners and policymakers for predicting the shopping modes in different future scenarios and formulating effective restriction measures.