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

Authors

DOI:

https://doi.org/10.5902/2179460X87221

Keywords:

Artificial neural networks, Hydrological modeling, Flood events, Multilayer perceptron

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.

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Author Biographies

Rennan Mendes de Moraes dos Santos Dias, Universidade do Estado do Rio de Janeiro

Holds a bachelor's degree in Mathematics (Bachelor's degree) with an emphasis on Applied and Computational Mathematics from the Fluminense Federal University - UFF/INFES (2019) and a master's degree in Computational Modeling in Science and Technology from the School of Metallurgical Industrial Engineering of Volta Redonda - EEIMVR/UFF/IINFES (2021). He is currently a PhD student at the Graduate Program in Computational Modeling (PPGMC) at the Polytechnic Institute - IPRJ/UERJ. Has experience in the area of Mathematics, Optimization Methods, Mathematics Applied to the Contaminant Transport Problem, Hydrological and Hydraulic Modeling, and Artificial Neural Networks.

Wagner Rambaldi Telles, Universidade Federal Fluminense

Holds a PhD in Computational Modeling from the State University of Rio de Janeiro - UERJ (2014) and a postdoctoral degree from the State University of Rio de Janeiro - UERJ (2015). Effective Professor at the Fluminense Federal University - PEB/INFES/UFF. He is also a permanent professor/researcher at the Academic Graduate Program in Computational Modeling - MCCT/EEIMVR/UFF. He also works as a collaborating professor at AmbHidro - IFF

Antônio José da Silva Neto, Instituto Politécnico do Rio de Janeiro

Ph.D. in Mechanical Engineering (North Carolina State University, 1993). Professor at IPRJ/UERJ (Adjunct Professor 1997-2012, Associate Professor 2012-2013, Full Professor 2013- ). Full member of the National Academy of Engineering (2017- ). He has been a Scientist of Our State (FAPERJ) since 2002. He has been a Proscientist at UERJ (Internal Competition) since 1997. He works in the area of Mechanical Engineering, with emphasis on Heat Transfer, and in Applied and Computational Mathematics, with emphasis on Numerical Methods.

References

AGEVAP (2017). Plano de Recursos Hídricos da Bacia do Rio Paraíba do Sul-Resumo, relatório contratual r-10 edn. Fundação COPPETEC Laboratório de Hidrologia e Estudos de Meio Ambiente.

Aghelpour, P., Varshavian, V. (2020). Evaluation of stochastic and artificial intelligence models in modeling and predicting of river daily flow time series. Stochastic Environmental Research and Risk Assessment, 34(1), 33–50.

ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000). Artificial neural networks in hydrology. ii: Hydrologic applications. Journal of Hydrologic Engineering, 5(2), 124–137.

Campolo, M., Soldati, A., Andreussi, P. (2003). Artificial neural network approach to flood forecasting in the river arno. Hydrological sciences journal, 48(3), 381–398.

Celeste, A., Chaves, V. S. (2014). Avaliação de algoritmos de otimização e funções objetivo para calibração automática do modelo chuva-vazão tank model. Ciência e Natura, 36(3), 527–537.

Dalkiliç, H. Y., Hashimi, S. A. (2020). Prediction of daily streamflow using artificial neural networks (anns), wavelet neural networks (wnns), and adaptive neuro-fuzzy inference system (anfis) models. Water Supply, 20(4), 1396–1408.

Elsafi, S. H. (2014). Artificial neural networks (anns) for flood forecasting at dongola station in the river nile, sudan. Alexandria Engineering Journal, 53(3), 655–662.

French, M. N., Krajewski, W. F., Cuykendall, R. R. (1992). Rainfall forecasting in space and time using a neural network. Journal of hydrology, 137(1-4), 1–31.

Hallak, R., Pereira Filho, A. J. (2011). Metodologia para análise de desempenho de simulações de sistemas convectivos na região metropolitana de são paulo com o modelo arps: sensibilidade a variações com os esquemas de advecção e assimilação de dados.

Revista Brasileira de Meteorologia, 26, 591–608. IBGE – Instituto Brasileiro de Geografia e Estatística (2023). Censo Brasileiro de 2022. Governo Federal, Rio de Janeiro, Brasil, URL https://cidades.ibge.gov.br/brasil/rj/rio-de-janeiro/panorama.

Kalin, L., Isik, S., Schoonover, J. E., Lockaby, B. G. (2010). Predicting water quality in unmonitored watersheds using artificial neural networks. Journal of environmental quality, 39(4), 1429–1440.

Kim, G., Barros, A. P. (2001). Quantitative flood forecasting using multisensor data and neural networks. Journal of Hydrology, 246(1-4), 45–62.

Kralisch, S., Fink, M., Flügel, W. A., Beckstein, C. (2003). A neural network approach for the optimisation of watershed management. Environmental Modelling & Software, 18(8-9), 815–823.

Kumar, M., Kumari, A., Kushwaha, D. P., Kumar, P., Malik, A., Ali, R., Kuriqi, A. (2020). Estimation of daily stage–discharge relationship by using data-driven techniques of a perennial river, india. Sustainability, 12(19), 7877.

Mosavi, A., Ozturk, P., Chau, K. w. (2018). Flood prediction using machine learning models: Literature review. Water, 10(11), 1536.

Tokar, A. S., Markus, M. (2000). Precipitation-runoff modeling using artificial neural networks and conceptual models. Journal of Hydrologic Engineering, 5(2), 156–161.

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Published

2024-11-07

How to Cite

Dias, R. M. de M. dos S., Telles, W. R., & Silva Neto, A. J. da. (2024). Hydrological modeling using artificial neural networks for flood event forecasting. Case study: Pomba river in Santo Antônio de Pádua - RJ. Ciência E Natura, 46(esp. 1), e87221. https://doi.org/10.5902/2179460X87221

Issue

Vol. 46 No. esp. 1 (2024): ERMAC e ENMC

Section

Special Edition 1

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