## 2 Materials and Methods
### Feature Construction  
To enable grid-level prediction of river siltation, a structured set of predictive features was constructed by integrating the spatial bathymetric data with the temporally aggregated  
\begin{table}
\begin{tabular}{c c c} \hline
**Data Type** & **Source** & **Description** \\ \hline Bathymetric data & Field survey & High-resolution riverbed elevation data collected via single-beam echo sounding at multiple time points; used to calculate grid-scale siltation volumes. \\ \hline Upstream water level & Daily upstream water level at the lock head; influences flow velocity and the sediment-carrying capacity of incoming water. \\ \hline Downstream water level & Daily downstream water level; affects sediment deposition and flushing capacity in the lower channel segment. \\ \hline Upstream threshold depth & Water depth at the upstream gate threshold; reflects potential sediment accumulation in critical navigation control zones. \\ \hline Downstream threshold depth & Shihutang hub operational data & Water depth at the downstream gate threshold; helps assess the degree of sedimentation impacting downstream navigability. \\ \hline Inflow discharge & Shihutang hub operational data & Total daily inflow volume; serves as a proxy for upstream hydrodynamic energy and potential sediment supply. \\ \hline Outflow discharge & Total daily outflow volume; used to analyze sediment export potential and downstream back-siltation dynamics. \\ \hline Vertical clearance & Distance between water surface and lock structure; indicates available clearance for vessel passage and reflects siltation impact on navigational safety. \\ \hline Meteorological data & Public weather websites & Daily maximum temperature, minimum temperature, sunshine duration, and wind speed; influence flow regimes and sediment dynamics. \\ \hline Remote sensing imagery & Satellite data & Optical imagery used to delineate the spatial extent of the river reach. \\ \hline \end{tabular}
\end{table}
Table 1: Data sources and descriptions.  
Figure 2: Field-surveyed bathymetric point measurements and grid-based river depth distribution derived from 30 m \(\times\) 30 m resampled data.  
hydro-meteorological variables. These features capture both the static morphological characteristics of the riverbed and the dynamic environmental conditions influencing sediment transport and deposition.  
The input variables were organized into three categories:  
Bathymetric feature: Each 30 m \(\times\) 30 m grid cell was assigned an initial riverbed elevation value derived from field surveys conducted at the beginning of each sampling period (i.e., 1 April or 17 May 2024). This variable provides essential spatial context related to sediment deposition potential.  
Hydrological features: A total of 28 features was extracted from daily measurements of the upstream and downstream water levels, threshold depths, inflow and outflow discharges, and vertical clearance. For each of the seven base variables, four statistical indicators--mean, maximum, minimum, and standard deviation--were computed over the corresponding time window to summarize the temporal variability in hydraulic conditions.  
Meteorological features: Four weather variables--maximum temperature, minimum temperature, sunshine duration, and wind force--were similarly summarized using the same four statistical measures, resulting in 16 additional features.  
In total, each sample (grid cell) is represented by 45 predictive features, comprising one spatial variable (initial bed elevation), 28 temporally aggregated hydrological descriptors, and 16 meteorological descriptors. This feature design enables the model to learn from both spatial heterogeneity and temporal dynamics under realistic data constraints.