Machine learning methodology for determination of sediment particle velocities over sandy and rippled bed

Abstract

The physics and quantification of sediment transport are still a challenge for scientists and engineers. Measurements of wave-induced sediment velocities may be conducted only in selected laboratories and require weeks of pre-tests and a very experienced team. In this study, student psychology-based optimization (SPBO) algorithm was applied to develop new integrated machine learning methods for the determination of wave-induced non-cohesive sediment particle velocities over a rippled bed by incorporating the outcome of particle image velocimetry. Easily measurable data comprising sediment characteristics, bedform details, and hydrodynamic conditions were used to train the machine learning models. The developed techniques determine well the sediment particle horizontal velocity over a horizontal profile. The analysis shows that the derived models nearly perfectly predict observed data. The proposed methodology provides insight into the physics of sediment processes and may also be applied to interpret measurement data and verify sediment transport models.