Abstract：
The estimation and prediction of the amount of sediment accumulated in reservoirs are imperative for sustainable 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 perceptron-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 (R2) 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 R2. 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 R2 (0.99) and RMSE (0.77); and R2 (0.97) and RMSE (1.80), respectively. On the other hand, the test data simulation of MLP-ANN and RFR demonstrated R2 (0.98) and RMSE (1.32); and R2 (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.
摘要：
水库沉积量的估算和预测对于可持续的水库沉积规划和管理以及最大程度地减少水库的储存能力损失至关重要。这项研究的主要目的是在Gibe-III储层中使用多层感知器-人工神经网络（MLP-ANN）和随机森林回归器（RFR）模型来估计和预测储层沉积，奥莫-吉贝河流域。用于估算和预测水库沉积的水文和气象参数包括年降雨量，年涌水量,最低水库水位,和水库储存能力。利用2014年至2022年的时间序列数据，采用MLP-ANN和RFR模型来估计和预测Gibe-III水库中积累的沉积物量。选择对于(80,20)训练测试方法具有0.97的确定系数(R2)的ANN架构N4-100-100-1,因为它在训练和测试(验证)模型中都显示出更好的性能。MLP-ANN和RFR模型的性能评估使用MAE进行，MSE,RMSE,和R2。模型评估结果表明，MLP-ANN模型优于RFR模型。关于MLP-ANN和RFR的列车数据模拟，显示R2(0.99)和RMSE(0.77)；R2(0.97)和RMSE(1.80)，分别。另一方面,MLP-ANN和RFR的测试数据模拟显示R2（0.98）和RMSE（1.32）；R2（0.96）和RMSE（2.64），分别。MLP-ANN模型模拟输出表明,未来Gibe-III型水库的泥沙堆积量将增加,2030-2031年达到110MT，2050-2051年达到130MT，2071-2072年超过137MT。
