Accurate prediction of sediment settling is critical for management of coastal ecosystems, but complex estuarine processes that influence sediment deposition and erosion present a major modelling challenge. This study explores a more efficient approach to simulating how particle size changes with dynamic sediment flocculation and thereby determines settling velocity. Environmental controls on in-situ particle size (median particle size D₅₀) were investigated using regression model trained on coeval measurements of salinity, shear rate, and suspended sediment concentration (SSC). A machine learning (ML) model was developed and integrated into a fully coupled current-wave-sediment model to simulate flocculation-dimensional response in particle size due to variations in shear rate, salinity and SSC. The integrated model framework demonstrates its reliability and accuracy when evaluated against the in-situ measurements, SSC derived from satellite observations, and a parametric flocculation model that only relates settling velocity to SSC.
 

machine learning, sediment modelling, sediment flocculation, settling velocity, remote sensing, in-situ measurement