Ensemble Approach for Hydrological Forecasting Based on Recurrent Neural Networks and Complex Networks

The São Francisco River Basin is crucial for Brazil’s agriculture, hydropower, and water security. However, climate change has intensified challenges like reduced water flow and frequent extreme events, threatening its socio-economic sustainability. This study aims to forecast flow in the São Francisco River Basin, enabling proactive decision-making to mitigate risks associated with both droughts and floods. To address these challenges, this study propose a novel methodology based on Artificial Intelligence (AI), combining Recurrent Neural Networks (RNN) and complex network techniques. The method creates new features and assigns importance weights to enhance the algorithm’s ability to generate probabilistic flow forecast. The results are promising, demonstrating the method’s ability to deliver accurate probabilistic forecasts. This research can support risk mitigation strategies and improve water resource management in the São Francisco Basin. Additionally, the proposed framework is scalable, offering potential applications to other critical watersheds facing similar challenges