Using Machine Learning and Hyperspectral Satellite to Monitor Suspended Sediment Concentration under Dredging Engineering

To address the threat of significantly elevated suspended sediment concentration (SSC) and its diffusion to coastal ecosystems caused by intense seabed sediment disturbance during large cross-sea transportation infrastructure construction. Using GF-5B hyperspectral satellite images and synchronous in-situ data, we developed SSC remote sensing inversion models for dredging-disturbed environments, including Random Forest (RF), Support Vector Machine (SVM), Linear Regression (LR) and empirical models. By comparing these models, we found that the RF model outperformed other models, with the RF model performing best (test set R ²  = 0.773, RMSE = 0.062 kg/m ³ ). By using the RF model to invert SSC from satellite data, we found that there was a high SSC in the dredging area (0.2-0.4684 kg/m ³ ) and a low level in surrounding areas (< 0.15 kg/m ³ ), with obvious spatial differences. This confirms that GF-5B hyperspectral data combined with machine learning enables high-precision SSC monitoring in dredging areas, providing a scientific basis for marine engineering ecological risk early warning and environmental protection optimization.