Recent advances on bedform research and application: Process-based to machine learning

Evolution and transition of bedforms in lowland rivers are micro-scale morphological processes that influence river management decisions. This work builds upon our past efforts that include physics-based modelling, physical experiments and the machine learning (ML) approach to predict bedform features, states as well as associated flow resistance. We revisit our past works and efforts on developing and applying numerical models, from simple to sophisticated, starting with a multi-scale shallow-water model with a dual-grid technique. The model incorporates an adjustment of the local bed shear stress by a slope effect and an additional term that influences bedform feature. Furthermore, we review our work on a vertical two-dimensional model with a free surface flow condition. We explore the effects of different sediment transport approaches such as equilibrium transport with bed slope correction and a non-equilibrium transport with pick-up and deposition. We revisit a sophisticated three-dimensional Large Eddy Simulation (LES) model with an improved sediment transport approach that includes sliding, rolling, and jumping based on a Lagrangian framework. Finally, we discuss about bedform states and transition that are studied using laboratory experiments as well as a theory-guided data science approach that assures logical reasoning to analyze physical phenomena with large amounts of data. A theoretical evaluation of parameters that influence bedform development is carried out, followed by classification of bedform type by using a neural network model.

In second part, we focus on practical application, and discuss about large-scale numerical models that are being applied in river engineering and management practices. Such models are found to have noticeable inaccuracies and uncertainties associated with various physical and non-physical reasons. A key physical problem of these large-scale numerical models is related to the prediction of evolution and transition of micro-scale bedforms, and associated flow resistance. The evolution and transition of bedforms during rising and falling stages of a flood wave have a noticeable impact on morphology and flow levels in low-land alluvial rivers. The interaction between flow and micro-scale bedforms cannot be considered in a physics-based manner in large-scale numerical models due to the incompatibility between the resolution of the models and the scale of morphological changes. The dynamics of bedforms and the corresponding changes in flow resistance are not captured. As a way forward, we propse a hydrid approach that includes application of the CFD models, mentioned above, to generate a large amount of data in complement with field and laboratory observations, analysis of their reliability based on which developing a ML model. The CFD models can replicate bedform evolution and transition processes as well as associated flow resistance in physics-based manner under steady and varying flow conditions. The hybrid approach of using CFD and ML models can offer a better prediction of flow resistance that can be coupled with large-scale numerical models to improve their performance. The reseach is in progress.