## 6 Conclusions and Future Directions  
The objective of the present research was to assess the effectiveness of AutoML models and remote-sensing-estimated rainfall datasets in forecasting the daily streamflow in the upper reaches of the Teles Pires River in the Amazon basin. Moreover, the aim was to improve the existing forecasting results obtained by the previous research works which addressed the same task in the context of the considered Amazon sub-region. A final review of the above-reported findings underscores the following main points:  
* The employed AutoML models have proven to be effective in forecasting the daily streamflow of the upper Teles Pires River up to 3 days. A specific group of AutoML models, including auto-sklearn, AutoKeras, and H2O AutoML, showed superior median performance in streamflow forecasting. Thus, the latter AutoML models represented a practical solution for predicting the daily streamflow in basins that lack extensive hydrometeorological data, like the Amazon one. * The streamflow of the upper Teles Pires River can be predicted using precipitation data estimated through remote sensing. Indeed, all the employed products showed comparable forecasting performances when they were used as input features for the AutoML models, even if PDIR-Now represented a preferable input feature due to its highest obtained performance score, fast refresh rate, and current active maintenance. In addition to the above-mentioned findings, in the present study, we introduced several novel approaches to enhance the application of AutoML for daily streamflow forecasting in the Amazon biomes. Such innovations include:  
* Customized feature engineering: we developed a novel feature engineering pipeline that integrates both time-lagged streamflow and the latest remote-sensing-estimated rainfall data. Such a pipeline was specifically tailored to address the challenges posed by the limited hydrological data available in the Amazon basin. * Hybrid data integration: unlike traditional approaches that rely solely on ground-based measurements, we incorporated multiple remote-sensing products, i.e., PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now, to enrich the training dataset. This hybrid integration significantly improved the AutoML models' ability to generalize and predict streamflow accurately. * Advanced model selection and hyperparameter tuning: a multi-stage AutoML process was employed. Such an approach not only automated the model selection and hyperparameter tuning but also ensured that the most optimal models were selected based on a comprehensive evaluation of multiple performance metrics. * Performance metrics customization: the standard performance metrics built in the AutoML models were customized to include MAE, RMSE, Bias, NSEI, and KGEI. Such a comprehensive set of metrics provided a holistic evaluation of the model's predictive capabilities. * Strong statistical analysis: an extensive statistical analysis was performed, including the Kruskal-Wallis test and Dunn post hoc test, to rigorously compare the performance of different AutoML models and feature sets. Such level of statistical scrutiny, to the best of our knowledge not done in previous studies, ensured the robustness and reliability of the presented findings. These innovative approaches underscore the significant contributions of our research in advancing the application of AutoML for hydrological forecasting in data-scarce regions like the Amazon basin. The developed AutoML models demonstrated reliable streamflow forecasts up to 3 days, serving as potential tools for decision-makers, particularly in relation to the appropriate planning and management of water resources within the Amazon basin. The ultimate goal of the latter is to improve the efficiency of reservoir operations for various purposes, including the hydroelectric production of electricity, flood control, providing water for human consumption, and proper irrigation. Despite the promising reported results, future developments could still arise from the present study.