ABSTRACT Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection.

中文翻译：

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新开发的集成机器学习模型在每日悬浮泥沙负荷预测和相关不确定性分析中的应用

摘要 Ensemble 机器学习模型已广泛应用于水利系统建模，作为结合多个决策树的稳健预测工具。在本研究中，提出了三种新开发的集成机器学习模型，即梯度提升回归 (GBR)、AdaBoost 回归 (ABR) 和随机森林回归 (RFR) 用于悬浮泥沙负荷 (SSL) 的预测，以及它们的预测性能和相关性。评估不确定性。密西西比河是世界主要河流之一，受沉积影响很大，它的 SSL 是根据河流流量 (Q) 和悬浮泥沙浓度 (SSC) 的日值预测的。基于性能指标和可视化，RFR 模型在预测性能上略有领先。