###### Abstract  
Accurate forecasting of river Sitlation is essential for ensuring inland waterway navigability and guiding sustainable sediment management. This study investigates the downstream reach of the Shihutang navigation power hub along the Ganjiang River in Jiangxi Province, China, an area characterized by pronounced seasonal sedimentation and hydrological variability. To enable fine-scale prediction, we developed a data-driven framework using a random forest regression model that integrates high-resolution bathymetric surveys with hydrological and meteorological observations. Based on the field data from April to July 2024, the model was trained to forecast monthly siltation volumes at a 30 m grid scale over a six-month horizon (July-December 2024). The results revealed a marked increase in siltation from July to September, followed by a decline during the winter months. The accumulation of sediment, combined with falling water levels, was found to significantly reduce the channel depth and width, particularly in the upstream sections, posing a potential risk to navigation safety. This study presents an initial, yet promising attempt to apply machine learning for spatially explicit siltation prediction in data-constrained river systems. The proposed framework provides a practical tool for early warning, targeted dredging, and adaptive channel management. keywords: river siltation; inland navigation; random forest; Ganjiang River; channel maintenance +
Footnote †: journal: Applied sciences  
water transport infrastructure, the province has established a progressively high-grade channel network. Navigation channels are vital arteries within this system, and their uninterrupted and efficient operation is essential for ensuring reliable waterborne transport. However, maintenance activities such as dredging and channel excavation--though necessary for meeting navigability standards--have introduced significant alterations to local hydrological regimes [5; 6]. These human interventions disrupt the natural flow-sediment equilibrium, increasing sediment transport complexity and intensifying the burden of post-dredging channel maintenance [7]. Among the major challenges in waterway management, siltation remains persistent and spatially heterogeneous. The evolution of sediment deposition in alluvial rivers is influenced by a variety of natural and anthropogenic factors, including river morphology, seasonal flow variation, sediment properties, and human interventions [8; 9; 10; 11]. Particularly in flood seasons, back-siltation can occur rapidly and unevenly, posing risks to channel depth and navigation safety. To address this issue, the traditional approaches, such as physical modeling and numerical simulation (e.g., MIKE21 and Delft3D), have been widely applied [12; 13]. These models are grounded in physical mechanisms and offer robust explanatory power, but are often limited by high computational costs, reliance on detailed boundary conditions, and sensitivity to parameter calibration. In highly dynamic and data-limited environments, their applicability is constrained. As an alternative, machine learning (ML) methods have gained attention for their ability to model complex nonlinear processes using observational data, without relying on explicit physical assumptions [14]. Recent studies have demonstrated the potential of ML techniques, such as artificial neural networks (ANNs), support vector machines (SVMs), and random forests (RFs), in sedimentation prediction for estuaries, reservoirs, and ports [15; 16; 17]. For example, the SVM has been used to model sediments from the Molinos River in Bogota, Colombia [16]; RF, the SVM, and the ANN were jointly applied to model suspended sediment in Tigris River, Baghdad [17]. These approaches have shown improved predictive accuracy and reduced dependency on domain-specific parameter calibration.