Streamflow estimation plays a significant role in water resources management, especially for flood mitigation, drought warning, and reservoir operation. Hence, the current study examines the prediction capability of three well-known machine learning algorithms (Support Vector Regression (SVR), Artificial Neural Network with backpropagation (ANN-BP), and Extreme Learning Machine (ELM)) for the monthly and daily streamflows of four rivers in the United States. For model development, three main predictor variables (P, T_max, and T_min) and their antecedent values were considered. The SVM-RFE feature selection method was used to select the most appropriate predictor variable.The performance of the developed models was tested using four evaluation statistics. The results indicate that (1) except some improvements, the accuracy of all models decreases at the daily scale compared to that at the monthly scale; (2) the SVR has the best performance among the three models at the monthly and daily scales, while the ANN-BP model has the worse performance; (3) the ELM has better generalization performance than the ANN-BP for streamflow simulation at the monthly and daily scales; and (4) all models fail to predict the streamflow for the Carson River as a snowmelt-dominated basin. Generally, findings of the current study indicate that the SVR model produces better results than the ELM and ANN-BP for streamflow simulation at the monthly and daily scales.

流量估算在水资源管理中起着重要作用，尤其是在防洪，干旱预警和水库运营中。因此，当前的研究检验了三种众所周知的机器学习算法（支持向量回归（SVR），带有反向传播的人工神经网络（ANN-BP）和极限学习机（ELM））对每月和每日流量的预测能力。在美国的四大河流中。对于模型开发，使用三个主要的预测变量（P，T _max和T _min）及其前值。使用SVM-RFE特征选择方法选择最合适的预测变量。使用四个评估统计量测试了开发模型的性能。结果表明：（1）除部分改进外，所有模型的日精度都比月度精度下降；（2）在三个模型中，SVR在月度和日度尺度上表现最佳，而ANN-BP模型则表现较差；（3）ELM在月和日尺度的流量模拟上具有比ANN-BP更好的泛化性能；（4）所有模型都无法预测作为融雪为主的盆地的卡森河的流量。通常，