Braided rivers are among the most dynamic natural Earth systems, with a rapid and complex morphological evolution. Limited understanding and inadequate algorithm implementation of specific processes affect the accuracy of physics-based models. This leads to uncertainties that complicate the effective design of interventions and protection measures. In recent years, artificial intelligence techniques rapidly advanced and the availability of satellite imagery products increased. This research sets a novel attempt to predict the planform evolution of braided rivers with deep learning using satellite images. The study focuses on the middle and lower reaches of the Brahmaputra-Jamuna River in India and Bangladesh. We developed JamUNet, a U-Net-based convolutional neural network (CNN). The model is trained with the Global Surface Water Dataset (GSWD) to classify each pixel as "Non-water" or "Water". Four images from the same month over four consecutive years were used as input. The fifth year image served as target. JamUNet demonstrates a general capability in capturing the planform evolution. Morphological processes like meander migration, channel abandonment, and confluence and bifurcation development are often well captured. However, temporal patterns are lacking. More complex phenomena, like channel formation and channel shifting, remain unpredicted. JamUNet also tends to underpredict the total areas of erosion and deposition. Overall, JamUNet achieves a 5-6% improvement compared to the benchmark method for which no morphological change occurs in metrics such as precision, recall, F1-score, and critical success index (CSI). Among these, recall is the most meaningful metric for evaluating the model performance. JamUNet can serve as a preliminary tool for water management authorities in India and Bangladesh. It can assist in prioritising bank protection in erosion-prone areas and support land reclamation projects and inland navigation. Caution is always advised due to the model tendency to underpredict erosion. More research is required to improve the current model. Nonetheless, deep-learning modelling seems a promising field of research. Testing alternative model architectures and incorporating additional data, such as water levels or river discharge, could improve the model performance.

Understanding morphodynamic processes and structures is essential for effective river management and enhancing our knowledge of river systems. River bars can be investigated through theoretical analyses, field measurements, experimental studies, or numerical modelling. While numerical modelling offers accuracy, it is computationally intensive, making multiple simulations or parameter calibrations both time-consuming and impractical. The numerical simulations follow physical laws and equations which make the runtime significantly high making instantaneous results in times of emergencies unfeasible.
Convolutional neural networks (CNNs), a type of data-driven modelling, have been employed to study the physical parameters defined by linear stability analysis. This study utilizes Delft3D simulations to generate diverse datasets, facilitating easier access and variability. A specific type of riverbar pattern, and alternate bars were chosen for simplicity. The CNN model takes initial bed levels as inputs and provides predictions for the next step of bed level or a time series, with velocity included an additional parameter to assess its influences on the model performance. The model is able to predict the bar behaviour with R2 being 0.99. The model can predict bar suppression or migration solely based on the initial bed levels provided. The model performance did not improve with an additional input parameter although this possibility can be explored with other architectures. However, the model currently lacks accuracy in making one-step-ahead predictions, potentially due to boundary issues within the numerical model or the CNN itself. Further optimization and exploration of additional methods are necessary. The integration of physical parameters into the training process may improve prediction accuracy. Strong conclusions cannot be drawn until additional research is conducted.