Comparison of Machine Learning Models for Predictive Mapping of Surface Sediments in Lianyungang Nearshore Area, China

High-precision sediment distribution maps are indispensable for nearshore sediment dynamics and ecology and nearshore resource management. Using grain-size data of surface sediments from the nearshore waters of Lianyungang and auxiliary datasets including bathymetric and hydrodynamic conditions, this study assessed Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR) for predicting sediment grain-size fractions and mapping sediment substrate types. All three models capture the spatial gradient of sediment grain size from fine to coarse from the nearshore to the offshore regions, but differ in preserving local heterogeneity and defining transition boundaries: XGBoost delivers the most balanced performance by preserving grain-size variability, reducing boundary mixing, and improving the identification of classes with limited samples; RF excels in robust delineation of gradual transitions, whereas SVR tends to produce fragmented boundaries and unstable performance for classes with limited samples. Feature importance reveals that hydrodynamic drivers dominate the spatial distribution of sand, whereas terrain indices are more influential for the clay distribution pattern, confirming the role of microtopography in modulating fine-sediment trapping. Overall, this study improves mapping accuracy and supports marine spatial planning and coastal infrastructure design.