## 3 Results
### Predicted Spatiotemporal Patterns of Siltration from July to December 2024  
The siltation prediction model developed using underwater topography and time-varying hydrological and meteorological features demonstrates a moderate predictive  
Figure 4: Riverbed elevation maps derived from three field surveys and corresponding siltation distributions during two observation periods: 1 April–17 May and 17 May–3 July 2024. Bathymetric data were collected along designated navigation routes and interpolated to 30 m \(\times\) 30 m grid. Blank areas labeled “NULL” represent regions with missing data due to shallow water or limited survey coverage. Only grid cells with valid siltation observations were included in model development, resulting in total of 746 valid sample points. Accompanying pie chart illustrates number of grid cells falling into different siltation level categories. “NULL” cells are excluded from pie chart analysis. Figure 5: Time series of daily water level and air temperature from April to December 2024, with statistical summaries for two sampling intervals. (**a**) Daily water level variations; (**b**) Daily maximum air temperature; (**c**) Statistical characteristics of water level and air temperature for April 1–May 17 and May 17–July 3 sampling intervals. performance. The model evaluation yields an MAE of 0.24, an MSE of 0.19, and a coefficient of determination (R\({}^{2}\)) of 0.58. While the model accuracy could be further improved with more extensive training data, the results indicate that this data-driven approach is effective in capturing the spatiotemporal characteristics of siltation under constrained data conditions. To assess the robustness of the modeling framework, we additionally compared the performance of the RF model with two alternative machine learning algorithms: the ANN and eXtreme Gradient Boosting (XGBoost). The RF model achieved the best predictive accuracy, with an R\({}^{2}\) of 0.58, outperforming XGBoost (R\({}^{2}\) = 0.41) and the ANN (R\({}^{2}\) = 0.32). These findings reinforce the suitability of RF for sediment prediction tasks involving mixed resolution features and limited samples, where ensemble tree-based models tend to generalize more reliably than deep or boosting-based methods. To further enhance model interpretability, a feature importance analysis based on the RF algorithm was conducted (see Figure 6). The results reveal that the initial bed elevation is by far the most influential predictor, reflecting the dominant role of local channel morphology in sediment deposition. In contrast, the hydrological and meteorological features--especially temperature, wind force, and sunshine duration--exhibit comparatively less, but non-negligible importance, indicating that broader environmental fluctuations also modulate siltation dynamics. These findings align with our understanding of physics and highlight the potential of integrating spatially fine-grained terrain data with regionally aggregated environmental indicators in data-driven sediment modeling. Figure 6: The feature importance scores (log-scaled) derived from the random forest model for predicting grid-level monthly siltation volumes. The most influential variable is the initial bed elevation, followed by several hydro-meteorological indicators. The features with zero importance have been excluded. Based on the trained model, the monthly siltation volumes were forecasted from July to December 2024 (see Figure 7 and Table 2). The results reveal an overall increasing trend in sediment accumulation during the summer months, with an average siltation depth of approximately 0.5 m per month. In terms of the total monthly siltation volume, the highest values were observed in July and September, while the lowest occurred in December. This temporal pattern is consistent with seasonal hydrological fluctuations, particularly higher inflows and sediment transport activity during the wet season (July-September), followed by reduced flow energy and sediment supply in the dry season (October-December).