River streamflow is an essential hydrological parameters for optimal water resource management. This study investigates models used to estimate monthly time-series river streamflow data at two hydrological stations in the USA (Heise and Irwin on Snake River, Idaho). Five diverse types of machine learning (ML) model were tested, support vector machine-radial basis function (SVM-RBF), SVM-Polynomial (SVM-Poly), decision tree (DT), gradient boosting (GB), random forest (RF), and long short-term memory (LSTM). These were trained and tested alongside a conventional multiple linear regression (MLR). To improve the estimation and model performance, hybrid models were designed by coupling the models with wavelet theory (W). The models performance was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), Nash-Sutcliffe efficiency (NSE), and Willmott’s index (WI). A side-by-side performance assessment of the stand-alone and hybrid models revealed that the coupled models exhibit better estimates of monthly river streamflow relative to the stand-alone ones. The statistical parameter values for the best model (W-LSTM4) during the test phase was RMSE = 36.533 m³/s, MAE = 26.912 m³/s, R² = 0.947, NSE = 0.946, WI = 0.986 (Heise station), and RMSE = 33.378 m³/s, MAE = 24.562 m³/s, R² = 0.952, NSE = 0.951, WI = 0.987 (Irwin station).

河流流量是优化水资源管理的重要水文参数。本研究调查了用于估计美国两个水文站（爱达荷州蛇河上的 Heise 和 Irwin）每月时间序列河流流量数据的模型。测试了五种不同类型的机器学习（ML）模型，支持向量机-径向基函数（SVM-RBF）、SVM-多项式（SVM-Poly）、决策树（DT）、梯度提升（GB）、随机森林（ RF）和长短期记忆（LSTM）。这些是与传统的多元线性回归 (MLR) 一起训练和测试的。为了提高估计和模型性能，通过将模型与小波理论 (W) 相结合，设计了混合模型。使用均方根误差 (RMSE)、平均绝对误差 (MAE)、决定系数 (R² )、纳什-萨特克利夫效率 (NSE) 和威尔莫特指数 (WI)。对独立模型和混合模型的并行性能评估表明，耦合模型相对于独立模型显示出更好的月河流流量估计值。测试阶段最佳模型（W-LSTM4）的统计参数值为RMSE = 36.533 m ³ /s，MAE = 26.912 m ³ /s，R ²  = 0.947，NSE = 0.946，WI = 0.986（Heise站） , RMSE = 33.378 m ³ /s, MAE = 24.562 m ³ /s, R ²  = 0.952, NSE = 0.951, WI = 0.987 (欧文站)。