Integrating Machine Learning with ADCP Data for Advanced Sediment Transport and Hydrodynamics Monitoring

Understanding sediment transport and hydrodynamic processes is critical for managing riverine and coastal systems, influencing navigation, flood risk, and sustainable sediment management. Traditional measurement approaches often rely on physical sediment sampling and manual data interpretation, which can be labour-intensive, spatially constrained, and time-consuming. This study presents a novel framework that combines Acoustic Doppler Current Profiler (ADCP)-derived data with machine learning (ML) techniques to enhance the monitoring and analysis of both sediment transport and hydrodynamics in open water environments.

Our dataset includes comprehensive hydrodynamic and acoustic parameters, such as bottom track velocity (BT), signal-to-noise ratio (SNR), acoustic backscatter (AB), depth, velocity standard deviation (SD), and mean flow speed. Exploratory analysis reveals significant relationships among these features, with BT,  SNR emerging as key proxies for sediment transport and hydrodynamic variability. Notably, BT shows moderate correlations with depth (r = 0.55) and SD (r = 0.36), underscoring its utility for characterizing flow conditions and sediment dynamics.

A machine learning framework is under development to analyse these relationships and predict sediment transport and hydrodynamic parameters. Initial exploratory findings highlight patterns in hydrodynamic variability and sediment transport proxies, laying the groundwork for advanced modeling efforts. Clustering algorithms reveal distinct flow regimes, and feature correlations suggest potential for predictive modeling of sediment dynamics.

This study demonstrates the potential of leveraging ADCP data for scalable and resource-efficient sediment and hydrodynamic monitoring. By integrating laboratory and field datasets, the proposed approach aims to enhance measurement capabilities and support the calibration and validation of numerical models. The findings hold significant implications for sustainable water resource management and the development of real-time hydro-morphological monitoring frameworks in diverse open water environments.