Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Ciência e Natura

DOI: 10.5902/2179460x87221

Authors: Rennan Mendes de Moraes dos Santos Dias, Wagner Rambaldi Telles, Antônio José da Silva Neto

Institutions: Universidade do Estado do Rio de Janeiro, Universidade Federal Fluminense, Instituto Politécnico do Rio de Janeiro

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: diamond

Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro