River meandering is the natural process that many lowland rivers undergo as a consequence of the alternation of bank erosion and accretion, which leads to the typical shape of the so-called meandering rivers. During the last decades, numerous modelling studies have been developed to reproduce their planform dynamics and to predict their future evolution. Most of these modelling approaches are physics-based, meaning that they solve the mathematical equations of shallow water open channel flow and fluvial sediment transport. Other types of modelling are very rare. Recent advances in artificial intelligence have led to promising results in many fields of science but their potential seems to have been so far rather unexplored in the prediction of meandering rivers morphodynamics. In this study, we have developed machine learning (hereinafter ML) models to compute the meander lateral migration rate based on training dataset: once the model has been trained with known migration rates and curvature values at two consecutive time steps, it is used to predict migration rates at the following time step. To this aim, the train and test dataset is coming from the outputs of a semi-analytical meander morphodynamic model which provides simulated evolving meandering planforms (described through the spatial curvature distribution) and migration rates computed through the excess near bank velocity. Such migration has been considered as the “Target” in the present study. The results for different models such as linear regression, feedforward neural network, SVM, and XGBoost were compared. It indicates that the “XGBoost” model with approximately 80 percent of accuracy in the prediction of the next time step, has the best result among them. This is just an opening chapter for the usage of ML in morphodynamics of meandering rivers and with advanced methods, there will be promising results ahead.

Confined meandering rivers are bounded between valleys and they are limited in their lateral migration compared to free meandering ones. In this study, we designed a model-based analysis to investigate types of confined meandering rivers. To simulate cases, we used the model developed by (Bogoni et al. 2017) based on a semi-analytical solution for flow and bed morphology ((Zolezzi and Seminara 2001). According to the result of (Lewin and Brindle 1977) the categorization of this type of meandering river should be based on the potential of evolution. We defined a confinement ratio as the ratio between the confined valley width and the meander belt width that the river would theoretically reach without geological confinement. Such unconfined meander belt width has been computed through long-term morphodynamic model simulations. This differs from previous work (Camporeale et al. 2005), semi-empirically suggesting an unconfined meander belt being nearly 3 times the modeled, linearly most unstable, intrinsic meander length. Our results indicate the existence of four different types of confined meandering rivers, which represents the most important point of this work. Compared to previous work, our classification adds a new class and reclassifies one of the previous types. Our proposed classes are; “Moderately confined condition” for confinement ratio in the range 0.1-1, “Strongly confined condition” for confinement ratio in the range 0.03-0.1. For a smaller confinement ratio than 0.03 we obtain a low sinuosity, single thread river, and for a confinement ratio greater than 1, we retrieve a free meandering river. Preliminary testing of our model-based classification with data from real confined meandering rivers shows promising results.