No abstract

Floods are a major natural disaster that causes significant losses to life and property, particularly in flood-prone areas like the north-western region of Bangladesh. Accurate and timely assessment of flood impacts is crucial for effective flood management and decision-making by stakeholders. In this study, we employed geospatial and remote sensing techniques to assess the impact of the 2020 flood in Kurigram and Lalmonirhat districts. We utilized synthetic aperture radar (SAR) data from Sentinel-1 and the Google Earth Engine platform to generate near real-time flood inundation maps. We used an automatic threshold method to calculate inundated areas from the SAR data. For land use/land cover (LULC) analysis, we used the high resolution sentinel-2 image using machine learning algorithms like artificial neural network (ANN), random forest (RF) and support vector machine (SVM). The best LULC method was selected using six performance matrices like kappa, accuracy, and F1 score. Our results showed that the highest inundation occurred in July, with more than 600,000 people exposed to floods, which is about 17% of the total population. The ANN model outperformed other models for LULC mapping with the accuracy of 0.94. The LULC map generated from our machine learning algorithm showed high accuracy, enabling us to identify the most vulnerable areas for floods and prioritize flood management efforts. The croplands were the most affected areas, with 53.7% being inundated in July. Our study highlights the potential of SAR data and machine learning algorithms for near real-time flood monitoring and accurate LULC mapping, which are essential for reliable flood management. The use of remote sensing data and geospatial techniques can facilitate more efficient and effective decision-making in response to natural disasters like floods. Our findings can assist decision-makers in developing appropriate flood management strategies to minimize the impacts of floods on life, property, and agricultural production.

The Ganges-Jamuna-Padma confluence is one of the world's most active confluences. The confluence of two of the world's greatest rivers, the Ganges and the Brahmaputra, makes this a globally significant site. Severe erosion along the banks has been caused by morphological changes in this region. Riverbank erosion is one of Bangladesh's most serious problems, as it necessitates costly intervention. Riverbank erosion in Bangladesh affects millions of people each year as a result of erosion in this confluence zone. As a result, it's critical to comprehend the confluence's morphological changing pattern. This study aims to quantify actual bank shifting around the confluence of the Ganges, Jamuna, and Padma in terms of shifting rate and area during a twenty-five-year period (1990-2015). To conduct this study the collected satellite image were geo-referenced and digitize bank lines from using ArcGIS program. The bank line is the linear structure that divides the river channel's outer border from the flood plains. The distance between the extreme margins of the left and right banks, including mid-channel sandbars, was measured to determine channel width variation. To assess the maturity of change, this time frame is subdivided into five phases, each lasting five years. In addition, the long-term shift from 1972 to 2015 is qualitatively noticeable. This morphological alteration was studied using LANDSAT satellite images. The research gives current and trustworthy information on the Ganga-Jamuna confluence's planform dynamics. This research will be useful in the planning and execution of drainage development plans and erosion control strategies in this critical confluence zone.