Abstract:
The estimation and prediction of the amount of sediment accumulated in reservoirs are imperative for sus
tainable reservoir sedimentation planning and management and to minimize reservoir storage capacity loss. The
main objective of this study was to estimate and predict reservoir sedimentation using multilayer percep
tron–artificial neural network (MLP-ANN) and random forest regressor (RFR) models in the Gibe-III reservoir,
Omo-Gibe River basin. The hydrological and meteorological parameters considered for the estimation and
prediction of reservoir sedimentation include annual rainfall, annual water inflow, minimum reservoir level, and
reservoir storage capacity. The MLP-ANN and RFR models were employed to estimate and predict the amount of
sediment accumulated in the Gibe-III reservoir using time series data from 2014 to 2022. ANN-architecture N4-
100-100-1 with a coefficient of determination (R
2
) of 0.97 for the (80, 20) train-test approach was chosen
because it showed better performance both in training and testing (validation) the model. The MLP-ANN and
RFR models’ performance evaluation was conducted using MAE, MSE, RMSE, and R
2
. The models’ evaluation
result revealed that the MLP-ANN model outperformed the RFR model. Regarding the train data simulation of
MLP-ANN and RFR shown R
2
(0.99) and RMSE (0.77); and R
2
(0.97) and RMSE (1.80), respectively. On the other
hand, the test data simulation of MLP-ANN and RFR demonstrated R
2
(0.98) and RMSE (1.32); and R
2
(0.96) and
RMSE (2.64), respectively. The MLP-ANN model simulation output indicates that the amount of sediment
accumulation in the Gibe-III reservoir will increase in the future, reaching 110 MT in 2030–2031, 130 MT in
2050–2051, and above 137 MTin 2071–2072.
