## 2 Related Works  
In recent years, researchers have employed several ML-based models to forecast the streamflow due to their high accuracy and flexibility [16, 17]. For instance, ANNs/DL-based models [43, 44], RF-based models [18, 19, 45], SVM-based models [46, 47, 48], and AutoML-based models [27, 28, 29] have been used to provide reliable streamflow forecasts. [PERSON] et al. [46] evaluated several ML models for predicting monthly streamflow using precipitation and temperature inputs, including the Optimally Pruned Extreme Learning Machine (OP-ELM), Least Square Support Vector Machine (LSSVM), Multivariate Adaptive Regression Splines (MARS), and M5 Model Tree (M5 Tree). The results obtained by the authors revealed that LSSVM and MARS-based models outperformed OP-ELM and M5 Tree models in streamflow prediction, even without the necessity for local input data. [PERSON] et al. [44] compared three ML-based techniques, i.e., Adaptive Neuro-Fuzzy Inference System (ANFIS), Genetic Programming (GP), and ANNs, to forecast the streamflow in the Shakkar watershed (Narmada Basin, India). The latter models incorporated past streamflow data and cyclic terms in the input vector to create suitable time-series models for streamflow forecasting. To evaluate the models' performances, standard time-series forecasting metrics were used. Results indicated that the ANFIS model achieved the best performance in time-series streamflow forecasting, with the GP model and the ANN model ranking second and third, respectively. The study highlighted that models incorporating cyclic terms significantly outperformed those relying solely on previous streamflow data. [PERSON] et al. [27] employed ML models to predict daily streamflow using hydrometeorological data related to rainfall, temperature, relative humidity, solar radiation, wind speed, and the one-day lag value of the streamflow. Among the employed ML models, i.e., bagging ensemble learning, boosting ensemble learning, Gaussian Process Regression (GPR), and the AutoML-GWL model [49], the bagging ensemble-learning model resulted as the most effective with a correlation coefficient \(R=0.80\) and Root Mean Square Error \(RMSE=218\). The authors even tested the Soil and Water Assessment Tool (SWAT) physical-based model; however, even the bagging ensemble-learning ML model demonstrated superior predictive strength (SWAT: \(R=0.82\); \(RMSE=281\)). [PERSON] et al. [28] developed a Multi-inflow Prediction Ensemble (MPE) model for dam inflow prediction using the auto-sklearn AutoML model [50]. The MPE model combined ensemble models for high and low inflow prediction to enhance the prediction accuracy. The study compared the MPE model performance with a conventional auto-sklearn-based ensemble model, finding that the MPE model significantly improved predictions during both flood and non-flood periods. The MPE model reduced RMSE and Mean Absolute Error (MAE) by 22.1% and 24.9%, respectively, and the designed model increased the coefficient of determination (\(R^{2}\)) and Nash-Sutcliffe efficiency coefficient by 21.9% and 35.8%, respectively. The results indicated that the MPE model could enhance water-resource management and dam operation, benefiting the environment and society. [PERSON] et al. [29] reconstructed long-term natural flow time-series for inflows into the Pearl River Delta (within the state of China) using global reanalysis datasets, observation data, and ensemble ML models. The study found that the quality of reconstruction provided by the employed AutoGluon AutoML model was superior with respect to the other employed ML models on large-scale datasets. Furthermore, the computed Indicator of Hydrologic Alteration and Range of Variability Analysis (IHA-RVA) revealed that climate variability, reservoir regulation, and human activities significantly altered the natural flow regime, typically smoothing the natural flow variability. As a final result, the research provided an efficient and reliable method for reconstructing natural river flows. [PERSON] et al.