## 2 Related Works
[30] evaluated the performance of the Large-Scale Distributed Hydrological Model (MGB-IPH) in forecasting water availability in the upper Teles Pires River basin. Despite the lack of climatic and hydrologic data in the region, the MGB-IPH model, calibrated and validated using data from three fluviometric stations, proved to be effective. The model performance was verified using the Nash-Sutcliffe Efficiency Index and the Bias metric. The results demonstrated that the MGB-IPH model could forecast the flow regimes in the upper Teles Pires basin with high reliability in terms of the employed metrics. Overall, the study indicated that the MGB-IPH model is applicable for water management in the basin, thus in areas with limited hydrometeorological data. [PERSON] et al. [19] assessed the performance of \(k\)-NN, SVM, RF, and ANNs ML-based models for daily streamflow prediction in a transitional region between the Savanna and Amazon biomes in the state of Brazil. The results reported by the authors indicated that the employed ML models provided reliable streamflow predictions for up to three days, even in the Amazon basin, where limited hydrometeorological data are available. [PERSON] et al. [20] developed ANNs ML-based models to predict floods in the Branco River within the Amazon basin. The ML models leveraged river levels and average rainfall data estimated from the remotely sensed rainfall PDIR-Now product. Hourly water level data were recorded by fluviometric telemetric stations of the Brazilian National Water Agency (Agencia Nacional de Aguas--ANA). The multilayer perceptron served as the framework for the ANNs, with the number of neurons in each layer determined through proper hyperparameter tuning methods. [PERSON] et al. [21] investigated ML models to predict streamflows for proper management of the Jucazinho Dam in the state of Pernambuco, Brazil. Relying on SVR, RF, and ANNs models, the study aimed to forecast the streamflow at the considered dam. Data normalization and Spearman's correlation were used to enhance the final ML model's accuracy. Evaluated using metrics such as the Nash-Sutcliffe efficiency coefficient and the coefficient of determination (\(R^{2}\)), the SVM model showed the best forecasting performance but was prone to underestimate long-term streamflow values, while RF and ANN models overestimated them likely due to overfitting. The study highlighted the effectiveness of ML in improving dam management in water-scarce regions such as Pernambuco, Brazil. As reported in the previous paragraphs, recent research works have significantly enhanced the application of ML models for streamflow forecasting, offering various advantages and limitations depending on the specific model and context. ANNs and DL models [43, 44] have gained attention due to their ability to model complex nonlinear relationships between inputs and outputs, making them particularly effective when ample data are available. However, such models often suffer from overfitting, particularly when applied to small datasets or regions with limited hydrometeorological data, as observedby [PERSON] et al. [44]. In contrast, RF-based models [18, 19, 45] offer robustness and interpretability, making them a preferred choice in scenarios where the dataset includes noise or missing values. However, their computational cost and the potential for overfitting in high-dimensional datasets remain challenges. SVM models [46, 47, 48] have shown remarkable accuracy in streamflow prediction, particularly for small-to-moderate datasets, thanks to their ability to manage nonlinear patterns. Yet, SVMs are computationally intensive and require careful tuning of hyperparameters, which can be a disadvantage in practical applications. AutoML approaches [27, 28, 29] have recently emerged as powerful tools that automate the model selection and tuning process, offering a balanced trade-off between performance and ease of use.