Hydrological modeling using artificial neural networks for flood event forecasting. Case study: Pomba river in Santo Antônio de Pádua - RJ

Flood prediction through hydrological modeling of watersheds remains an emerging need in society, particularly in regions highly affected by these extreme events. Models based on artificial neural networks have demonstrated significant potential for addressing this issue due to their simplicity and agility. In this study, a model was developed using a multilayer perceptron network for predicting river discharge and water level based on the previous day's river state and precipitation forecast. The Pomba river in the city of Santo Antônio de Pádua-RJ was investigated due to its regular occurrence of flood events that impact the entire population. Metric and graphical results showed the model's strong ability to estimate discharge and water levels throughout the year at a station with limited data. On the other hand, the model encountered difficulties in accurately estimating peak values.