## 1 Introduction
Designing streamflow ML forecasting models that both require minimal training data and can automate training, testing, tuning, and final model selection is crucial for hydrological studies in world regions with limited data available, such as the Amazon [19, 20, 30]. Indeed, the Amazon basin has a lower density of hydrometeorological stations compared to other regions in Brazil and worldwide [19, 20]. Moreover, the existing network of stations suffers from issues related to limited historical data, uneven distribution, data series inconsistencies, and limited measurement periods [19, 20]. Such data gaps are partly due to the region's vast size and the inaccessibility of areas where monitoring stations are located, such as national parks and indigenous lands where the Amazon rainforest is constantly preserved. In the presented scenario, ML models have highly proven effectiveness in accurately representing the hydrological behavior of the considered Amazon region even with limited data, as demonstrated by previous studies [19, 20], and AutoML models could further enhance such forecasting performance. Water resources in the Amazon basin hold significant value, primarily for the production of hydroelectric energy through the construction of dams [31]. Unfortunately, the latter process leads to several environmental issues, including biodiversity loss [32], local social impacts [33], flooding of rainforest areas [34], and disruptions of the region hydrological cycle [35]. Currently, three Hydroelectric Power Plants (HPPs)--Sinop, Coldier Pesqueiro do Gil, and Teles Pires--are operational in the upper Teles Pires River basin [36]. Furthermore, the construction of two additional HPPs in the Amazon region--the Jirau and Santo Antonio dams (on the Madeira River, Rondonia) and the Belo Monte dam (on the Xingu River, Para)--recently indicated a growing demand for hydroelectric production in the considered region [37]. As a consequence, the Brazilian National Electric System Operator (Operator Nacional do Sistema Eletrico--ONS) started to manage water accumulation reservoirs and to compute streamflow predictions relying on stochastic models [19, 38]. As well as stochastic models, the ONS also employed conceptual models like SMAP [39, 40], but such models reported limited precision [38, 40]. Thus, the potential of other ML-based forecasting models, such as the ones represented by AutoML warrants exploration for improving the performance of the critical streamflow prediction task in the Amazon region. The importance of computing accurate streamflow forecasts cannot be understated for effective water-resource planning and management in the Amazon basin region. Thus,given the constraints of the existing hydrometeorological data and the requirement for reliable streamflow predictions, the objective of the present study is to exploit AutoML methods to effectively forecast the daily streamflow in the selected geographical area. Furthermore, the objective is even to improve the forecasting performances of the already existing ML-based forecasting methods previously applied in the same context. Towards the latter aims the present research relied both on data sourced from several rain and streamflow gauge stations and rainfall products estimated through remote sensing, in line with previously investigated approaches [19, 20, 21, 30]. The employed estimates included Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), PERSIANN--Cloud Classification System (PERSIANN-CCS), PERSIANN--Climate Data Record (PERSIANN-CDR), and PERSIANN--Dynamic Infrared Rain rate near real-time (PDIR-Now), where the latter one represents a recently introduced product just recently tested in the context of streamflow forecasting [41, 42].