Analyzing the association between the hydrodynamics and bank erosion along the Padma River: 2020 monsoon floods

Abstract

The 2020 Monsoon floods submerged two-thirds of Bangladesh, profoundly affecting the stable char areas along the Padma River. This study addresses the under-explored link between Monsoonal flood hydrology and river morphological changes, introducing a novel approach that integrates one-dimensional steady flow hydraulic modeling, dual-sensor satellite imagery analysis (Sentinel-1 SAR and Sentinel-2 optical), and multiple machine learning techniques to assess flood impacts on bank erosion in the most affected reaches. Multiple linear regression (MLR), random forest (RF), artificial neural networks (ANN), and support vector machines (SVM) along with other statistical analyses, were employed to elucidate relationships between hydraulic variables and bank erosion. Results indicated a gradual decrease in river discharge-related variables from July to October, with peak erosion in July, primarily along the right banks and concave areas. In analyzing the relationships between hydraulic variables and bank erosion, the ANN model outperformed others, identifying flow area, stream power, and shear stress as the most influential predictors through importance analysis. This integrated approach offers a transferable framework for analyzing flood-induced bank erosion, enhancing riverbank erosion understanding crucial for flood risk management strategies in Bangladesh and similar river systems worldwide.