## 5 Discussion  
The experimental results confirmed that the usage of time-delayed rainfall and streamflow data provided reliable input features to train AutoML models designed to forecast future streamflow. Indeed, rainfall data plays a crucial role within hydrological forecasting models, being the primary way of water influx into a hydrographic basin [19, 66]. Moreover, the time-delayed streamflow provides a representation of the moisture condition in watersheds, given that runoff happens post-soil saturation [19, 67]. As previously noted by [PERSON] et al. [19] and [PERSON] et al. [20], the application of ML for predicting the daily streamflow in the Amazon basin offers significant benefits, particularly due to the lack of available hydromerological data. Indeed, even the findings described in the present study indicate that ML-based models are a viable solution in the context of basins with inadequate hydrological monitoring and a lack of fundamental information required for the proper implementation of physical and/or conceptual rainfall-runoff models. According to the results shown in Figure 6, the executed Dunn post hoc tests revealed two groups of AutoML models with similar in-group median forecasting performances: Group 1 includes auto-sklearn, AutoKeras, and H2O AutoML, while Group 2 includes TPOT and MLBox. In addition to the results of the executed Dunn tests, even the computed AutoML model score AutoML \(\overset{\text{T}}{\text{score}}\), highlighted the difference in forecasting performance between the two found groups of AutoML models: Regarding the scored combinations of employed models and features reported in Table 5, over the 15 th ranked position (included) only combinations with AutoML models within Group 1 do appear. On the other hand, under the 16 th ranked position (included), only combinations with models within Group 2 are listed. Indeed, it must be noted that if pairwise score differences are computed between the sorted scored combinations, the highest score difference found is between \begin{table}
\begin{tabular}{c l l c} \hline \hline
**Ranking** & **AutoML Model** & **Input Features** & **AutoML\(\overset{\text{T}}{\text{score}}\)** \\ \hline
1 & auto-sklearn & PDIR-Now & 23.79 \\
2 & AutoKeras & PDIR-Now & 23.81 \\
3 & H2O AutoML & PERSIANN-CCS & 23.83 \\
4 & H2O AutoML & Thiessen & 24.40 \\
5 & auto-sklearn & PERSIANN & 26.13 \\
6 & H2O AutoML & PDIR-Now & 26.15 \\
7 & auto-sklearn & PERSIANN-CCS & 26.19 \\
8 & AutoKeras & PERSIANN-CDR & 26.40 \\
9 & auto-sklearn & PERSIANN-CDR & 28.97 \\
10 & H2O AutoML & PERSIANN & 30.14 \\
11 & auto-sklearn & Thiessen & 30.42 \\
12 & H2O AutoML & PERSIANN-CDR & 30.78 \\
13 & AutoKeras & PERSIANN & 32.25 \\
14 & AutoKeras & PERSIANN-CCS & 37.32 \\
15 & AutoKeras & Thiessen & 38.48 \\ \hline
16 & TPOT & PERSIANN-CCS & 64.02 \\
17 & MLBox & Thiessen & 79.74 \\
18 & TPOT & PERSIANN-CDR & 90.26 \\
19 & MLBox & PDIR-Now & 96.56 \\
20 & MLBox & PERSIANN & 98.70 \\
21 & MLBox & PERSIANN-CCS & 106.76 \\
22 & TPOT & PERSIANN & 113.39 \\
23 & MLBox & PERSIANN-CDR & 113.65 \\
24 & TPOT & Thiessen & 117.77 \\
25 & TPOT & PDIR-Now & 133.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The AutoML models and respective input features ranked by the AutoML\(\overset{\text{T}}{\text{score}}\) score. For the sake of readability, the table is sorted in ascending order according to the computed score. The ranking of the combinations, from 1 to 25, is reported in the first column. Furthermore, a horizontal line divides the table relying on the two groups of models found with the Dunn post hoc tests: Group 1 includes auto-sklearn, AutoKeras, and H2O AutoML; Group 2 includes TPOT and MLBox. the 15 th and 16 th combination, with a score difference of 25.54. As already described in Section 4, not only did the AutoML models within Group 1 show median forecasting performances superior with respect to the models contained in Group 2, but they even achieved a minimal loss of median forecasting performance when increasing the time-lag prediction \(l\) up to \(l=3\).