Author(s): Diego Silva Piedra; Monica Fossati; Pablo Ezzatti
Linked Author(s): Mónica Fossati
Keywords: Forecasting; Machine learning; LightGBM; Río de la Plata
Abstract: PronUy_RPFM is an operational tool designed by researchers from the Institutes of Fluid Mechanics and Environmental Engineering (IMFIA) and Computing (INCO) at the Faculty of Engineering (FIng), University of the Republic (UdelaR) in Uruguay, to predict tide levels in the Río de la Plata and its maritime front. The tool provides hourly sea level forecasts for the next three days, which are publicly available on its dedicated website. Originally based on the TELEMAC2D finite element numerical model, this study presents the advances in the application of machine learning (ML) methods within the PronUy_RPFM forecasting stage. Specifically, the Light Gradient Boosting Machine (LightGBM) framework was implemented for tide level forecasting, resulting in the new models: PronUy_LightGBM1 and PronUy_LightGBM2. We detail the structure and present the results of these new models. The effectiveness of PronUy_LightGBM1 is evaluated by comparing its predictions with measured data from tide gauges located along the Uruguayan and Argentine coasts.
Year: 2026