Performance improvement of machine learning models via wavelet theory in estimating monthly river streamflow

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 (R-2), 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 m(3)/s, MAE = 26.912 m(3)/s, R-2 = 0.947, NSE = 0.946, WI = 0.986 (Heise station), and RMSE = 33.378 m(3)/s, MAE = 24.562 m(3)/s, R-2 = 0.952, NSE = 0.951, WI = 0.987 (Irwin station).