LEITE, Jandecy Cabral2025-02-032022LOPES, G. F.; NASCIMENTO, M. H. R.; LIMA, A. A.; MORAES, N. M.; PINTO JÚNIOR, J. R. L.; ALENCAR, A. P. B.; ALENCAR, D. B. Application of the Nonlinear Autoregressive Model with Exogenous Inputs for River Level Forecast in the Amazon. International Journal for Innovation Education and Research, v. 10, n. 3, p. 304-323, mar. 2022. DOI: 10.31686/ijier.vol10.iss3.2022.https://rigalileo.itegam.org.br/handle/123456789/328This work is justified by three key aspects: the use of Artificial Intelligence, the problem of floods in the Amazon, and the application of technology in decision-making. Environmental impacts caused by economic and social factors, such as floods and river ebbs, affect public health and agricultural production, as well as increase erosion in risk areas. Thus, the use of AI to predict river levels can minimize these impacts by enabling effective preventive actions. The study applies the Nonlinear Autoregressive Model with Exogenous Inputs (NARX) to forecast river levels in the Amazon, using easily accessible variables implemented in MATLAB. The results indicate that the NARX model can accurately predict river level fluctuations, contributing to environmental impact mitigation strategies.PDF.PrevisãoNível do rioNARXInteligência ArtificialModelagem hidrológicaArtigoEngenharia Ambiental / Modelagem Hidrológica