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The Paraı́ba do Sul river flows through the most important industrial region of Brazil and its basin is characterized by conflicts of multiple uses of its water resources. The prediction of its natural flow has strategic value for water management in this basin. This research investigates the applicability of the two machine learning methods (Random Forest and Artificial Neural Networks) for daily streamflow forecasting of the Paraı́ba do Sul River at lead times of 1-7 days. The impact of fluviometric and pluviometric data from other basin sites on the quality of the forecast is also evaluated. Resumo. O rio Paraı́ba do Sul flui através da mais importante região industrial do Brasil e sua bacia é caracterizada por conflitos de usos múltiplos de seus recursos hı́dricos. A previsão de sua vazão natural possui valor estratégico para a gestão da água nesta bacia. Essa pesquisa investiga a aplicabilidade dos métodos Random Forest e Redes Neurais Artificiais na modelagem de vazão média diária de uma estação fluviométrica do rio Paraı́ba do Sul com um horizonte de previsão de até 7 dias. Avalia-se o impacto dos dados fluviométricos e pluviométricos de outros locais da bacia na qualidade da previsão.

Daily streamflow forecasting for Paraíba do Sul river using machine learning methods with hydrologic inputs