Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flood susceptibility models (FSMs) may produce varying spatial predictions. However, there have not been many attempts to address these uncertainties because identifying spatial agreement in flood projections is a complex process. This study presents a framework for reducing spatial disagreement among four standalone and hybridized ML-based FSMs: random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and hybridized genetic algorithm-gaussian radial basis function-support vector regression (GA-RBF-SVR). Besides, an optimized model was developed combining the outcomes of those four models. The southwest coastal region of Bangladesh was selected as the case area. A comparable percentage of flood potential area (approximately 60% of the total land areas) was produced by all ML-based models. Despite achieving high prediction accuracy, spatial discrepancy in the model outcomes was observed, with pixel-wise correlation coefficients across different models ranging from 0.62 to 0.91. The optimized model exhibited high prediction accuracy and improved spatial agreement by reducing the number of classification errors. The framework presented in this study might aid in the formulation of risk-based development plans and enhancement of current early warning systems.

Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water levels to aid flood forecasting is underexplored. Deep learning and attention-based models have shown high potential for accurately forecasting floods over space and time. The present study aims to develop a long short-term memory (LSTM) network and its attention-based architectures to predict flood water levels in the rivers of Bangladesh. The models developed in this study incorporated gauge-based water level data over 7 days for flood prediction at Dhaka and Sylhet stations. This study developed five models: artificial neural network (ANN), LSTM, spatial attention LSTM (SALSTM), temporal attention LSTM (TALSTM), and spatiotemporal attention LSTM (STALSTM). The multiple imputation by chained equations (MICE) method was applied to address missing data in the time series analysis. The results showed that the use of both spatial and temporal attention together increases the predictive performance of the LSTM model, which outperforms other attention-based LSTM models. The STALSTM-based flood forecasting system, developed in this study, could inform flood management plans to accurately predict floods in Bangladesh and elsewhere.

Assessing flood risk is challenging due to complex interactions among flood susceptibility, hazard, exposure, and vulnerability parameters. This study presents a novel flood risk assessment framework by utilizing a hybridized deep neural network (DNN) and fuzzy analytic hierarchy process (AHP) models. Bangladesh was selected as a case study region, where limited studies examined flood risk at a national scale. The results exhibited that hybridized DNN and fuzzy AHP models can produce the most accurate flood risk map while comparing among 15 different models. About 20.45% of Bangladesh are at flood risk zones of moderate, high, and very high severity. The northeastern region, as well as areas adjacent to the Ganges–Brahmaputra–Meghna rivers, have high flood damage potential, where a significant number of people were affected during the 2020 flood event. The risk assessment framework developed in this study would help policymakers formulate a comprehensive flood risk management system.

Background: Having inadequate health care systems and poor socio-economic infrastructure, Bangladesh has been braving to contain the impact of current COVID-19 pandemic since March, 2020. To curb the diffusion of COVID-19, the local government has responded to the outbreak by enforcing a set of restricted measures on economic and social activities across the country. Objectives: Here, we aim to assess the propagation of COVID-19 by estimating the coronavirus active cases and mortality rate in two major business hubs of Bangladesh, namely Dhaka and Chittagong city under flexible lockdown conditions. Methods: We apply a data-driven forecasting model using Susceptible, Exposed, Infected, Recovered and Deaths status through time to deal with coronavirus outbreak. Results: The epidemiological model forecasts the dire consequences for Dhaka city with 2400 death cases at the end of December, 2020, whereas Chittagong city might experience 14% more deaths than Dhaka if the severe restrictions are not implemented to control the pandemic. Conclusion: Although lockdown has a positive impact in reducing the diffusion of COVID-19, it is disastrous for human welfare and national economies. Therefore, a unidirectional decision by the policymakers might cost a very high price on either way for a lower-middle-income country, Bangladesh. In this study, we suggest a fair trade-off between public health and the economy to avoid enormous death tolls and economic havoc in Bangladesh.

Brain magnetic resonance imaging (MRI) offers intricate soft tissue contrasts that are essential for diagnosing diseases and conducting neuroscience research. At 7 Tesla (7T) magnetic field intensity, MRI enables increased resolution, enhanced tissue contrast, and improved SNR, compared to MRI collected from the commonly employed 3 Tesla (3T) MRI scanners. However, the exorbitant expenses associated with 7T MRI scanners hinder their broad use in research and clinical facilities. Efforts are underway to develop algorithms that can generate 7T MRI from 3T MRI to achieve better image quality without the need for 7T MRI machines. In this study, we have adopted a cycle consistent generative adversarial network (CycleGAN)-based approach for 3T MRI to 7T MRI translation, and vice versa, using a recently published dataset of paired T1-weighted MR images collected at 3T and 7T from a total of ten subjects. Various CycleGAN architectures were experimented with and compared on this dataset. The best performing CycleGAN architecture successfully produced the reconstructed images with a high level of accuracy based on different quantitative and qualitative evaluation criteria. Utilizing a post-processing technique, the best performing model generated 7T MRI from 3T MRI with a structural similarity index measure (SSIM) of 83.80%, peak SNR (PSNR) of 26.25, normalized mean squared error (NMSE) of 0.0088 and normalized mean absolute error (NMAE) of 0.0630. Utilizing CycleGAN to convert images from 3T to 7T MRI has shown a substantial improvement in MRI resolution, setting the stage for advancements in more informative and precise diagnostic imaging.