River meanders are one of the most recurrent and varied patterns in
fluvial systems. Multiple attempts have been made to detect and
categorise patterns in meandering rivers to understand their shape and
evolution. A novel data-driven approach was used to classify single-bend
meanders. A dataset containing approximately 10 million single-lobe
meander bends was generated using the Kinoshita curve. A neural network
autoencoder was trained over the curvature energy spectra of
Kinoshita-generated meanders. Then, the trained network was then tested
on real meander bends extracted from satellite images, and the energy
spectrum in the meander curvature was reconstructed accurately thanks to
the autoencoder architecture. The meander spectrum reconstruction was
clustered, and three main bend shapes were found associated with the
meander datasets, namely symmetric, upstream-skewed, and
downstream-skewed. The autoencoder-based classification framework
allowed bend shape detection along rivers, finding the dominant pattern
with implications on migration trends. By studying the shift in the
prevailing bend shape over time, cutoff events were approximately
forecast along the Ucayali River, whose migration was remotely sensed
for 32 years. Overall, the method proposed opens the venue to
data-driven classifications to understand and manage meandering rivers.
Bend shape classification can thus inform restoration and flood control
practices and contribute to predicting meander evolution from satellite
images or sedimentary records.