Daily streamflow forecasting for Paraíba do Sul river using machine learning methods with hydrologic inputs - Gorodetskaya, Yulia; da Fonseca, Leonardo Goliatt; Tavares, Gisele Goulart; Ribeiro, Celso Bandeira de Melo (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2018: Anais do XV Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2018.4413, 2018)
The Paraíba 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 Paraíba 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.