An empirical investigation into predicting the volume of water accumulation in dams that supply the Metropolitan Region of Recife - Silva, Rodolfo Amorim C. da; Albuquerque, Rodolfo Viegas de; Melo Neto, Milton Tavares de; Oliveira, João Fausto Lorenzato de (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2025: Anais do XXII Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2025.11789, 2025)
This article reports an empirical investigation on the prediction of the volume of water accumulated in dams, comparing the performance of traditional statistical methods, machine learning algorithms, and dynamic and static ensemble approaches, applied to volume time series, seeking to understand if there is any model that stands out from the others. The study showed that LSTM networks, isolated or used in ensemble, seem to be a sufficiently robust model to reproduce time series with a hydrological context.