Abstract:
This study presents a comprehensive framework for monitoring and managing sedimentation
in River Malaba, a transboundary river shared by Uganda and Kenya within the Malaba subcatchment.
The
research
integrated
hydrological
modeling,
hydraulic
simulation
and
machine
learning
to address challenges related to sediment yield estimation, sediment dynamics and
real-time sediment monitoring.
The Soil and Water Assessment Tool (SWAT) was used to simulate catchment hydrology and
estimate sediment yield. Model calibration and validation were performed using SUFI-2, with
a Nash-Sutcliffe Efficiency (NSE) of 0.72 and R² of 0.76 for calibration, and NSE of 0.68 and
R² of 0.74 for validation. The simulation revealed an average annual sediment yield of
142,248.7 tons with spatial variability across sub-basins ranging from 1.72 to 86.93 tons/km²,
highlighting sediment hotspots in the catchment. Hydraulic modeling was carried out using
HEC-RAS 6.4.1 to evaluate sediment transport and deposition patterns along a critical reach
of the river within Kalait Sub-County. The model achieved an NSE of 0.70 and R² of 0.73
during calibration (1999–2008) and NSE of 0.69 and R² of 0.71 during validation (2009–2022).
Key outputs included sediment routing characteristics, trap efficiency estimations and the
identification of erosion-prone and deposition-prone reaches along the river corridor. To
facilitate real-time sediment prediction, a soft sensor was developed using a Multi-Layer
Perceptron (MLP) Artificial Neural Network. Rainfall, temperature and flow velocity were
used as input parameters due to their influence on sediment concentration. The model was
trained and optimized using grid search and achieved R² scores of 0.90 for the training set, 0.80
for validation, and 0.85 for the testing set, indicating strong generalization and predictive
performance. A sediment concentration heatmap was also generated using geospatial
coordinates and predicted concentrations. River segments were ranked and categorized into
low, moderate and high sediment concentration zones. The heatmap visually identified critical
locations that require dredging thus offering actionable insights for maintenance planning.
This integrated approach offers a robust decision support tool for sediment monitoring and
management in river basins.
