Work

U-Net-Based Daytime and Nighttime Prediction of Surface Suspended Sediment Concentrations in Wenzhou Coastal Waters

Year: 2026

Type: article

Abstract: This study constructs a time-dependent model to predict the nighttime suspended sediment concentration near Wenzhou based on the convolutional neural network U-Net, which integrates the high-resolution Delft3D (version 4.03.01) hydrodynamic model and GOCI satellite observation data. The model’s prediction accuracy is significantly improved by replacing the original tide level with the tide level variation and increasing the temporal resolution of the flow field to 15 min via sensitivity analysis of the model’s input parameters. The validation results show that the model can maintain high consistency with GOCI observations in short-term prediction, with a structural similarity index (SSIM) of 0.82. For multi-hour continuous nighttime predictions, while quantitative uncertainty increases with the forecast horizon, the model successfully captures the spatial evolution patterns and maintains stable structural characteristics. The model effectively provides missing remote sensing nighttime observations as well as a new method for full-cycle prediction of nearshore SSC.

Source: Journal of Marine Science and Engineering

DOI: 10.3390/jmse14030282

Authors: Miao Zhang, Peixiong Chen, Bangyi Tao, Xin Zhou

Institutions: Ministry of Natural Resources, Zhejiang Ocean University, Second Institute of Oceanography, State Key Laboratory of Satellite Ocean Environment Dynamics

Language: English

Topic: Coastal and Marine Dynamics

Subfield: Earth-Surface Processes

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

Estimated APC paid: $3,137