Suspended sediment load (SSL) flowing into a reservoir contributes to the overall safety of dam. Owing to the complexity and stochastic nature of sedimentation, accurate prediction of reservoir SSL inflow is still challenging. Moreover, research and application of machine learning (ML) techniques for reservoir sedimentation are still deficient. A comprehensive evaluation of six ML models for a reservoir SSL inflow prediction was performed in this study. ML techniques including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), radial basis function neural network (RBFNN), support vector machine (SVM), genetic programming (GP), and deep learning (DL) were applied to develop predictive models of daily SSL inflow at Sangju Weir, South Korea. Significant input vectors for each model were selected with streamflow, water temperature, water stage, reservoir outflow for different time lags. Model performances were evaluated using various statistical indices including the coefficient of determination (R²), mean absolute error (MAE), percentage of bias (PBIAS), Willmott index (WI), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and Pearson correlation coefficient (PCC). The best input combinations were found to be unique for each ML model, but all six models performed reasonably well for SSL inflow predictions. ANN model outperformed other models with R² = 0.821, MAE = 4.244 tons/day, PBIAS = 0.055, WI = 0.891, NSE = 0.991, RMSE = 11.692 tons/day, PCC = 0.826. The models were ranked based on their SSL prediction capabilities as ANN > ANFIS > DL > RBFNN > SVM > GP from best to worst. The findings are expected to be useful for future dam safety and risk assessment, and for achieving sustainability of reservoir operation through comprehensive sediment management.

流入水库的悬浮泥沙负荷（SSL）有助于大坝的整体安全。由于沉积的复杂性和随机性，准确预测储层SSL流入仍具有挑战性。而且，用于储层沉积的机器学习（ML）技术的研究和应用仍然很不足。在这项研究中，对用于储层SSL流入预测的六个ML模型进行了综合评估。应用了ML技术，包括人工神经网络（ANN），自适应神经模糊推理系统（ANFIS），径向基函数神经网络（RBFNN），支持向量机（SVM），遗传编程（GP）和深度学习（DL）开发韩国Sangju Weir每日SSL流量的预测模型。通过流选择每个模型的重要输入向量，水温，水位，水库出水时间不同。使用各种统计指标（包括确定系数（R²），平均绝对误差（MAE），偏差百分比（PBIAS），威尔莫特指数（WI），纳什-苏克利夫效率（NSE），均方根误差（RMSE）和皮尔森相关系数（PCC）。最佳输入组合对于每个ML模型都是唯一的，但是对于SSL流入预测，所有六个模型均表现良好。ANN模型优于其他模型，R ²  = 0.821，MAE = 4.244吨/天，PBIAS = 0.055，WI = 0.891，NSE = 0.991，RMSE = 11.692吨/天，PCC = 0.826。这些模型基于其SSL预测能力从最佳到最差，分别为ANN> ANFIS> DL> RBFNN> SVM> GP。预期这些发现将对未来的大坝安全和风险评估以及通过全面的沉积物管理实现水库运行的可持续性有用。