A data-driven classification of meander bends based on their energy spectrum.

Meandering planforms are commonly observed in fluvial systems. A meander consists of a series of two alternate bends connected at the points of inflexion by relatively short, almost straight crossings. The presence of single-thread meandering rivers exhibiting a continuous sequence of such curves is widespread in alluvial floodplains. The study of river meanders has thus fascinated the scientific community, which, for a long time, has tried not only to classify them but also to quantify the complexity of meandering planforms and model their morphodynamic evolution.

The idea of classifying meandering rivers has a long history. It has produced a series of non-dimensional parameters to identify a meander (i.e., half-meander amplitude, asymmetry index, half-meander sinuosity). Nevertheless, two main problems arise from the existing methodologies. They are too complicated to encompass as many shapes as possible or lack physical insight into hydraulic and sedimentological parameters.

We propose a data-driven approach to address this classification issue, mixing physics-based information and machine-learning algorithms. In our approach, we consider the spatial distribution of meander curvatures and analyse it using different continuous wavelet transforms, getting the energy spectrum for each meander. This physics-based information is then firstly processed as an unsupervised visual classification problem using a neural-network autoencoder mix with cluster algorithms. The output of this first step analysis consists of two pre-trained algorithms that can classify the energy spectrum of pictures of planform curvatures and, therefore, the meander planform shape.

The algorithms will be trained with a series of dimensionless, synthetically generated meanders and t subsequently tested with both natural and simulated meanders. The final aim is to identify automatically which type of meanders characterise a given river reach at a certain time. This methodology also has the potential to be extended to Spatiotemporal distributions of channel-axis curvature, thus unravelling key aspects of meandering dynamics, as well as identifying similarities between reaches of different rivers or between observed and synthetically generated river planforms.