Hybrid neural network for daily streamflow projection on a sub-seasonal timescale

Everton Santos Castro; Carlos Eduardo Sousa Lima; Antônio Duarte Marcos Junior; Cleiton da Silva Silveira; Francisco das Chagas Vasconcelos Junior

doi:10.5327/Z2176-94782495

Authors

Everton Santos Castro Federal University of Ceará (UFC) - Brazil https://orcid.org/0000-0003-2544-4874
Carlos Eduardo Sousa Lima Federal University of Ceará (UFC) - Brazil https://orcid.org/0000-0003-2199-3382
Antônio Duarte Marcos Junior Federal University of Ceará (UFC) - Brazil https://orcid.org/0000-0001-7015-1355
Cleiton da Silva Silveira Federal University of Ceará (UFC) - Brazil https://orcid.org/0000-0003-3303-5157
Francisco das Chagas Vasconcelos Junior Fundação Cearense de Meteorologia e Recursos Hídricos (FUNCEME) - Brazil https://orcid.org/0000-0002-1558-8383

DOI:

https://doi.org/10.5327/Z2176-94782495

Keywords:

Streamflow forecast, Artificial Neutral Network, Wavelet Transform, Sub-seasonal forecast

Abstract

Forecasts of hydrological variables are extremely important for water resource management in regions that are particularly vulnerable to the effects of climate change and climate variability. The aim of this study was to develop a hybrid neural network capable of producing daily streamflow forecasts on a sub-seasonal scale. This hybrid neural network consists of an artificial neural network with inputs preprocessed by the wavelet transform (WANN). The WANN was tested in the Três Marias, Sobradinho, and Retiro Baixo reservoirs, located in the São Francisco River Basin (SFRB). The obtained results show that WANN was highly accurate in short-term forecasts (7–28 days); however, for long-term forecasts (35 and 42 days), there was a significant drop in performance, especially during the transition periods to the rainy season and in the dry months. The comparison between the performance metrics of the WANN forecasts and the National Electricity System Operator (ONS) operational models for the Três Marias, Sobradinho, and Retiro Baixo basins showed that WANN outperformed all these models. The results obtained show that WANN is a valuable tool for addressing the complex and dynamic challenges of hydrology, making it essential for decision-making on water resource management.

Downloads

Download data is not yet available.

References

Agarwal, S.; Roy, P.; Choudhury, P.; Debbarma, N., 2022. Estudo comparativo sobre a previsão de vazão de rios usando GMNN e GMNN baseado em wavelet. Journal of Water and Climate Change, v. 13 (9), 3323-3337. https://doi.org/10.2166/wcc.2022.226.

Akujuobi, C.M., 2022. Wavelets and wavelet transform systems and their applications. Springer International Publishing. Berlin/Heidelberg, Germany.

Al-Juboori, A.M., 2023. Prediction of hydrological drought in semi-arid regions using a novel hybrid model. Water Resources Management, v. 37 (9), 3657-3669. https://doi.org/ 10.1007/s11269-023-03520-1.

Arsenault, R.; Martel, J.L.; Brunet, F.; Brissette, F; Mai, J., 2023. Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models. Hydrology and Earth System Sciences, v. 27 (1), 139-157. https://doi.org/10.5194/hess-27-139-2023.

Bellier, J.; Whitin, B., Scheuerer, M.; Brown, J.; Hamill, T.M., 2023. A multi-temporal-scale modulation mechanism for the postprocessing of precipitation ensemble forecasts: Benefits for streamflow forecasting. Journal of Hydrometeorology, v. 24 (4), 659-673. https://doi.org/10.1175/JHM-D-22-0119.1.

Benedetto, J.J.; Michael, W.F., 2021. Wavelets: mathematics and applications. CRC Press, [S.l.].

Bettencourt, P.; Fulgêncio, C.; Grade, M.; Alcobia, S.; Monteiro, J.P.; Oliveira, R.; Leitão, J.C.; Leitão, P.C.; Fernandes, P.A.; Sousa, S.; Brites, S.; Fernandes, J.; Simões, J.; Scarton, M.; Santiago, E.; Aguiar, R.; Giffoni, M.; Melo, F.; Paes, A.C., 2016. Plano de recursos hídricos da bacia hidrográfica do rio São Francisco. Recursos Hidricos, v. 37 (1), 73-80. https://doi.org/10.5894/rh37n1-cti3.

Bevacqua, E.; Zappa, G.; Lehner, F.; Zscheischler, J., 2022. Precipitation trends determine future occurrences of compound hot–dry events. Nature Climate Change, v. 12 (4), 350-355. https://doi.org/10.1038/s41558-022-01309-5.

Ekwueme, B.N., 2024. Deep neural network modeling of river discharge in a tropical humid watershed. Earth Science Informatics, v. 17 (2), 1161-1177. https://doi.org/10.1007/s12145-023-01219-w.

Ferreira, N.C.; Chou, S.C.; Dereczynski, C., 2023. Evaluation of subseasonal precipitation simulations for the Sao Francisco River Basin, Brazil. Climate, v. 11 (11), 213. https://doi.org/10.3390/cli11110213.

Freire, P.K.M.M.; Santos, C.A.G.; Silva, G.B.L., 2019. Analysis of the use of discrete wavelet transforms coupled with ANN for short-term streamflow forecasting. Applied Soft Computing, Elsevier, v. 80, 494-505. https://doi.org/10.1016/j.asoc.2019.04.024.

Höge, M.; Scheidegger, A.; Baity-Jesi, M.; Albert, C.; Fenicia, F., 2022. Improving hydrologic models for predictions and process understanding using neural ODEs. Hydrology and Earth System Sciences, v. 26 (19), 5085-5102. https://doi.org/10.5194/hess-26-5085-2022.

Hunt, K.M.; Matthews, G.R.; Pappenberger, F.; Prudhomme, C., 2022. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States. Hydrology and Earth System Sciences, v. 26 (21), 5449-5472. https://doi.org/10.5194/hess-26-5449-2022.

Hurkmans R.T,; Van Den Hurk, B.A; Schmeits, M.; Wetterhall, F. Pechlivanidis, I.G., 2023. Seasonal streamflow forecasting for fresh water reservoir management in the Netherlands: An assessment of multiple prediction systems. Journal of hydrometeorology, v. 24 (7), 1275-1290. https://doi.org/10.1175/JHM-D-22-0107.1

Khazaeiathar, M.; Schmalz, B., 2025. Advancing Flood Forecasting With Wavelet‐LSTM: The Role of Nonlinearity in Discharge Prediction. Journal of Flood Risk Management, v. 18 (4), e 70148. https://doi.org/10.1111/jfr3.70148.

Li, W.; Pan, B.; Xia, J.; Duan, Q., 2022. Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts. Journal of Hydrology, v. 605, 127301. https://doi.org/10.1016/j.jhydrol.2021.127301.

Li, X.; Xu, W.; Ren, M.; Jiang, Y.; Fu, G., 2022. Hybrid CNN-LSTM models for river flow prediction. Water Supply, v. 22 (5), 4902-4919. https://doi.org/10.2166/ws.2022.170.

Maceira, M.; Damázio, J.; Ghirardi, A.; Dantas, H., 1999. Periodic arma models applied to weekly streamflow forecasts. In: IEEE. Power Tech Budapest 99. Abstract Records. (Cat. No. 99EX376). [S.l.], p. 86.

Nearing, G.; Cohen, D.; Dube, V.; Gauch, M.; Gilon, O.; Harrigan, S.; Matias, Y., 2024. Global prediction of extreme floods in ungauged watersheds. Nature, v. 627 (8004), 559-563. https://doi.org/10.1038/s41586-024-07145-1.

Operador Nacional do Sistema Elétrico (ONS), 2015. ONS Relatório Anual de Avaliação das Previsões de Vazões – 2015. [S.l.], ONS.

Ougahi, J.H; Rowan, J.S., 2025. Enhanced streamflow forecasting using hybrid modelling integrating glacio-hydrological outputs, deep learning and wavelet transformation. Scientific Reports, v. 15 (1), 2762. https://doi.org/10.1038/s41598-025-87187-1.

Paiva, L.F.G.; Acioli, G.C.L., 2007. Previsão de vazões com o modelo cpins – cálculo e previsão de vazões naturais e incrementais a sobradinho. In: Anais do I Workshop de previsão de vazões. ONS, Rio de Janeiro.

Quedi, E.; Fan, F.; Siqueira, V.; Collischonn, W.; Paiva, R.; Petry, I.; Gama, C.; Silveira, R.; Paranhos, C.; Freitas, C., 2024. Sub-seasonal streamflow forecasts for hydropower dams in the Brazilian Eletrical Interconnected System. RBRH, v. 29, e7. https://doi.org/10.1590/2318-0331.292420230109.

Ribeiro, S.C., 2017. Caracterização geoambiental da sub-bacia do rio salgado na mesorregião sul cearense–parte i–clima e arcabouço geológico. Geoconexões, v. 1, 4-16. https://doi.org/10.15628/geoconexoes.2017.6290.

Santana, A.S.; Santos, G.R., 2020. Impactos da seca de 2012-2017 na região semiárida do Nordeste: notas sobre a abordagem de dados quantitativos e conclusões qualitativas. Boletim Regional, Urbano e Ambiental (BRUA), v. 22 (22), 119-129. https://doi.org/10.38116/brua22art9.

Santos, A.L.L., 2022. Previsão de vazão afluente da UHE-Tucuruí por redes neurais recorrentes LSTM. Orientador: Raphael Barros Teixeira. 2022. [9], 44 f. Undergraduate Thesis (Bachelor’s Degree in Electrical Engineering) – Faculdade de Engenharia Elétrica, Campus Universitário de Tucuruí, Universidade Federal do Pará, Tucuruí. Retrieved 2025-10-20, from https://bdm.ufpa.br/handle/prefix/4578.

Saraiva, S.V.; Carvalho, F.O.; Santos, C.A.G.; Barreto, L.C.; Freire, P.K.M.M., 2021. Daily streamflow forecasting in Sobradinho Reservoir using machine learning models coupled with wavelet transform and bootstrapping. Applied soft computing, v. 102 (107081), 107081. https://doi.org/10.1016/j.asoc.2021.107081.

Sharma, P.; Singh, S.; Sharma, S.D., 2022. Artificial neural network approach for hydrologic river flow time series forecasting. Agricultural Research, v. 11 (3), 465-476. https://doi.org/10.1007/s40003-021-00585-5.

Sherstinsky, A., 2020. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, v. 404, 132306. https://doi.org/10.1016/j.physd.2019.132306.

Silva, M.V.M.; Silveira, C.S.; Costa, J.M.F.; Martins, E.S.P.R; Júnior, F.C.V, 2021. Projection of climate change and consumptive demands projections impacts on hydropower generation in the São Francisco River Basin, Brazil. Water, v. 13 (3), 332. https://doi.org/10.3390/w13030332.

Silva, M.V.M; Silveira, C.S; Costa, J.M.F; Martins, E.S.P.R; Júnior, F.C.V., 2020. Projections of climate change in streamflow and affluent natural energy in the Brazilian hydroelectric sector of CORDEX models. RBRH, v. 25. https://doi.org/10.1590/2318-0331.252020200020.

Souza, T.S.D.; Nascimento, P.D.S., 2022. Sequential climatic water balance applied in the hydrographic region of Paraguaçu, in Brazil. Sustainable Water Resources Management, v. 8 (1), 18. https://doi.org/10.1007/s40899-022-00608-1.

Tian, B.; Kong, X.; Wu, G., 2024. The application of the LSTM neural networks on the hydrology forecast. In: Proceedings of the 2024 6th International Conference on Pattern Recognition and Intelligent Systems. pp. 93-97. https://doi.org/10.1145/3689218.3689233.

Tuğrul, T.; Hinis M.A., 2025. Performance enhancement of models through discrete wavelet transform for streamflow forecasting in Çarşamba River, Türkiye. Journal of Water and Climate Change, v. 16 (2), 736-756. https://doi.org/10.2166/wcc.2025.709.

Wilks, D.S., 2011. Statistical methods in the atmospheric sciences. Academic Press, [S.l.]. v. 100.

Yilmaz, M., Tosunoğlu, F., Kaplan, N.H., Üneş, F., Hanay, Y.S., 2022. Predicting monthly streamflow using artificial neural networks and wavelet neural networks models. Modeling Earth Systems and Environment, v. 8 (4), 5547-5563. https://doi.org/10.1007/s40808-022-01403-9.

Zanial, W.N.C.W., Malek, M.B.A., Reba, M.N.M., Zaini, N., Ahmed, A.N., Sherif, M.; Elshafie, A., 2023. River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network. Applied Water Science, v. 13 (1), 28. https://doi.org/10.1007/s13201-022-01830-0.

Zhao, X.; Wang, H.; Bai, M.; Xu, Y.; Dong, S.; Rao, H.; Ming, W., 2024. A comprehensive review of methods for hydrological forecasting based on deep learning. Water, v. 16 (10), 1407. https://doi.org/10.3390/w16101407.