Convolutional Neural Networks for modelling riverbed level variations

Abstract

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.