## 5 Discussion
Indeed, for the models contained in the first group, median test forecasting performances were contained in the same order of magnitude for MAE and RMSE for each set lag. On the other hand, for the AutoML models contained in the second group the order of magnitude of the latter two metrics tended to increase by one unit when increasing the prediction time-lag up to \(l=3\). Minimal variations were observed with respect to the Bias metric. Regarding NSEI and KGEI, the median performances of AutoML models within Group 1 were greater than the value of 0.90 for each employed time-lag, while for models of Group 2, they tended to reach inferior values, even below 0.80. The structural differences between the AutoML models led to varying performances in streamflow forecasting. Indeed, auto-sklearn, AutoKeras, and H2O AutoML consistently outperformed TPOT and MLBox across most of the employed metrics. The superior performance of auto-sklearn, AutoKeras, and H2O AutoML can be attributed to their advanced optimization techniques and the ability to integrate a broader range of ML algorithms and hyperparameters. In contrast, TPOT's reliance on genetic programming and MLBox's limited model selection and feature processing capabilities resulted in less optimal predictions. Such differences underscore the importance of selecting appropriate AutoML models based on their structural characteristics to achieve reliable and accurate streamflow forecasts. Regarding the above-reported findings, as already noted by previous literature, it was found that TPOT could underperform if compared to other AutoML models due to several reasons: (1) The TPOT training phase is highly computationally expensive [68]. Indeed, the TPOT GP search process involves evolving a potentially huge population of ML models and respective configurations over several generations, which can be significantly more time-consuming with respect to the techniques leveraged by other AutoML models. The latter point is particularly relevant in the case when large datasets or complex ML models are employed; (2) The TPOT performance can be highly sensitive to the difficult setting of its GP-related parameters, such as population size and mutation rate, which can lead to sub-optimal results [69]; (3) Last, but not least, TPOT may not perform optimally in situations where the optimal ML pipeline requires advanced ML models that are not included in the TPOT available configuration, such as advanced DL models. Indeed, the pipeline elements leveraged by TPOT only include algorithms and ML models borrowed from the well-known Python scikit-learn library [55]. As with TPOT, MLBox may underperform if compared to other AutoML models for several reasons: (1) One of the primary reasons is represented by the lack of available ML models to be leveraged, with respect to the other employed AutoML models in the present study [58]. Indeed, MLBox only includes LightGBM, RF, Extra Trees, Trees, Bagging, AdaBoost, and Linear ML models. The limited availability of usable ML models could limit the MLBox capability of capturing complex patterns in the training data that more advanced ML models are capable of, such as Stacking, SVM, ANNs, and DL models; (2) Additionally, the MLBox feature selection process is limited to standard feature processing techniques, which could lead to sub-optimal feature sets being used for ML models training; (3) Finally, while the MLBox limited amount of adjustable parameters could be seen as an advantage towards the fast developing of ML models, it could also represent a drawback in scenarios where a careful selection of the ML pipeline settings is required. As a result, the high MLBox emphasis on ease of usage may result in a trade-off with the resulting ML model performance, particularly when complex or large-scale datasets are leveraged. The AutoML models employed in the present research were selected based on their proven effectiveness in handling limited training data and their previous remarkable performances reported in the previous literature [23, 24, 25].