## 4 Discussion  
The findings of this study demonstrate that sedimentation dynamics in the Shihutang reach of the Ganjiang River are highly responsive to both seasonal hydro-meteorological conditions and channel morphological characteristics. The proposed RF regression model achieved moderate predictive accuracy, effectively capturing the magnitude of and spatial variability in river siltation processes. Although the model's performance could be further enhanced through the inclusion of more extensive training data and higher-resolution features (e.g., real-time flow velocities and sediment concentrations), its ability to generate reliable and interpretable results under limited-data conditions validates the practical value of machine learning approaches in fluvial sediment prediction. Figure 8: Spatial classification of inundated and exposed areas based on zero-value depth threshold from July to December 2024. The spatiotemporal patterns predicted by the model reveal clear seasonal contrasts. During the wet season (from July to September), the siltation volumes increased markedly, with a few grid cells showing accumulations exceeding 4 m. While these may include isolated outliers, they indicate the potential emergence of high-risk siltation zones, especially under intensified rainfall and elevated temperatures, which accelerate sediment transport and deposition. In contrast, the dry season (from October to December) was characterized by lower siltation rates and more spatially concentrated low-value zones, especially in the upstream section of the channel. These seasonal differences highlight the heterogeneity and temporal variability inherent in riverine sedimentation processes, driven largely by climatic and hydrological fluctuations. One of the most critical insights from this study concerns the impact of cumulative siltation combined with declining water levels on navigation safety. The model projections suggest that beginning in August, sediment buildup along the channel margins progressively narrows the effective navigable width. By December, extensive shoaling and bed exposure are expected in the lower reaches, posing a high risk of navigation failure if mitigation measures are not implemented in a timely manner. Notably, the water level thresholds presented in the analysis likely underestimate the actual navigability constraints, as effective channel clearance requires a minimum depth that exceeds zero-value elevation. From a management and planning perspective, these results underscore the urgent need for proactive sediment monitoring, targeted dredging operations, and flexible water level regulation, particularly in the high-risk zones identified by the model. Moreover, integrating such data-driven prediction models into real-time waterway management systems could significantly enhance the capacity for early warning, risk forecasting, and adaptive long-term planning, especially under climate variability and during increasing anthropogenic disturbances. Several limitations should be acknowledged. First, although the bathymetric data were collected at a high spatial resolution, the hydrological and meteorological variables were spatially uniform daily aggregates, limiting the model's capacity to reflect fine-scale hydrodynamics. Second, important physical drivers, such as sediment grain size, concentration, substrate type, and dredging history, were not included due to data unavailability. Third, the model was trained on observations from April to June (relatively high-flow months), but applied to predict July-December (transitional-to-low-flow months), which may introduce the extrapolation uncertainty. These factors may contribute to the model's moderate R\({}^{2}\), and its reduced sensitivity to localized or extreme siltation events. Fourth, the six-month predictions were not directly validated against post-survey observations or actual dredging records, limiting the ability to assess real-world accuracy.