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Published (Version of record)CC BY V4.0, Open Access
Published (Version of record)CC BY V4.0, Open
Journal article
Integrating Machine Learning into a Fully Coupled Current-Wave-Sediment Model: Characterizing Particle Size in the Settling Process in Estuaries of the Great Barrier Reef, Australia
Ocean modelling, Vol.198, pp.1-7
12/2025
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Abstract
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 introduces a proof-of-concept framework that integrates machine learning (ML) into environmental simulations to improve accuracy and efficiency by modelling dynamic sediment flocculation processes and their influence on particle size, enabling a more precise determination of settling velocity. Environmental factors influencing in-situ sediment particle size were used to train a regression model based on coeval measurements of three key parameters: salinity, shear rate and suspended sediment concentration (SSC). This regression model was developed using ML and integrated into a fully coupled current-wave-sediment model to simulate the flocculation response to these three parameters. The integrated model framework demonstrates its reliability and accuracy when evaluated against the in-situ measurements, satellite-derived SSC for the Fitzroy Estuary (Great Barrier Reef), and a parametric flocculation model that only relates settling velocity to SSC. We present an example of the ML-based approach outperforming a parametric model by capturing nonlinear particle-hydrodynamic interactions while maintaining computational efficiency, enabling high-resolution SSC simulations. This work demonstrates an advancement for hybrid modelling using rapidly evolving ML applications, offering a scalable tool for sediment transport and water quality management.
Details
- Title
- Integrating Machine Learning into a Fully Coupled Current-Wave-Sediment Model: Characterizing Particle Size in the Settling Process in Estuaries of the Great Barrier Reef, Australia
- Creators
- Ziyu Xiao - CSIROAUBRDaniel N. Livsey - U.S. Department of Agriculture (United States, El Reno)Thomas Schroeder - CSIROAUBRDavid Blondeau-Patissier - CSIROAUBRRodrigo Santa Cruz - CSIRO Data61 (Australia, Brisbane)Su Jiasheng - CSIROHBDehai Song - Ocean University of ChinaXiao Hua Wang - UNSW CanberraGeoffrey Carlin - CSIROAUBRAndrew D.L. Steven - CSIROAUBRJoseph R. Crosswell - CSIROAUBR
- Publication Details
- Ocean modelling, Vol.198, pp.1-7
- Publisher
- Elsevier Ltd; London
- Grant note
- CSIRO R+ CERC FellowshipGreat Barrier Reef FoundationQueensland Department of Environment and ScienceSpecial Funds for Taishan Scholar Project: tsqn202211056 Australian Institute of Marine ScienceBureau of MeteorologyNational Natural Science Foundation of China: 41876088
Z.X. was supported by CSIRO R+ CERC Fellowship and field work was funded by the eReefs project, a public-private collaboration between CSIRO, the Australian Institute of Marine Science, the Bureau of Meteorology, and the Great Barrier Reef Foundation and the Queensland Department of Environment and Science. Observation data at MMP station is operated under the Great Barrier Reef Marine Monitoring Program for Inshore Water Quality (Australian Institute of Marine Science (AIMS) (2016) ) . D.S. was funded by National Natural Science Foundation of China (No. 41876088) and Special Funds for Taishan Scholar Project (No. tsqn202211056) . The model simulations have been carried out on the CSIRO Petrichor HPC cluster. We wish to thank EUMETSAT for the provision of Sentinel-3A OLCI data.
- Identifiers
- 991013309820502368
- Copyright
- © 2025 The Authors.
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article