Monitoring sediment transport is essential for managing and maintaining rivers. Estimation of the sediment load in rivers is fundamental for the study of sediment movement, erosion, and flood control. In the current study, three machine learning models—multi-layer perceptron (MLP), multi-layer perceptron-stochastic gradient descent (MLP-SGD), and gradient boosted tree (GBT)—were utilized to estimate the suspended sediment load (SSL) at the St. Louis (SL) and Chester (CH) stations on the Mississippi River, U.S. Four evaluation criteria including the Correlation Coefficient (CC), Nash Sutcliffe Efficiency (NSE), Scatter Index (SI), and Willmott's Index (WI) were utilized to evaluate the performance of the used models. A sensitivity analysis of the models to the input variables revealed that the current day discharge variable had the most effect on the SSL at both stations, but in the absence of current-day discharge data (Q_t), a combination of input parameters including $\mathrm{S}\mathrm{S}{\mathrm{L}}_{\mathrm{t}-3},\mathrm{S}\mathrm{S}{\mathrm{L}}_{\mathrm{t}-2},\mathrm{S}\mathrm{S}{\mathrm{L}}_{\mathrm{t}-1},Q_{t-3},Q_{t-2},Q_{t-1}$ can be used to estimate the SSL. The comparative outcomes indicated the high accuracy of MLP-SGD-5 model with a CC of 0.983, SI of 0.254, WI of 0.991, and NSE of 0.967 at station CH and the MLP-SGD-6 model with a CC of 0.933, SI of 0.576, WI of 0.961, and NSE of 0.867, respectively, at station SL. The results of MLP models were improved by SGD optimization. Therefore, the MLP-SGD method is recommended as the most accurate model for SSL estimation.

监测沉积物的运输对于河流的管理和维护至关重要。估算河流中的泥沙负荷是研究泥沙运动，侵蚀和防洪的基础。在当前研究中，利用三种机器学习模型（多层感知器（MLP），多层感知器随机梯度下降（MLP-SGD）和梯度增强树（GBT））来估计悬浮泥沙量（SSL） ），位于美国密西西比河上的圣路易斯（SL）和切斯特（CH）站，四个评估标准包括相关系数（CC），纳什萨特克利夫效率（NSE），分散指数（SI）和威尔莫特指数（WI） ）用于评估所用模型的性能。Q _t），输入参数的组合，包括$\mathrm{小号}\mathrm{小号}{\mathrm{大号}}_{\mathrm{Ť}-3}，\mathrm{小号}\mathrm{小号}{\mathrm{大号}}_{\mathrm{Ť}-\mathrm{2个}}，\mathrm{小号}\mathrm{小号}{\mathrm{大号}}_{\mathrm{Ť}-\mathrm{1个}}，{问}_{Ť-3}，{问}_{Ť-\mathrm{2个}}，{问}_{Ť-\mathrm{1个}}$可用于估计SSL。比较结果表明，CH站的CC值为0.983，SI为0.254，WI为0.991，NSE为0.967的MLP-SGD-5模型和CC为0.933，SI的MLP-SGD-6模型具有较高的准确性SL站分别为0.576，WI为0.961和NSE为0.867。通过SGD优化可以改善MLP模型的结果。因此，推荐使用MLP-SGD方法作为SSL估计的最准确模型。