Abstract
Water resources are essential for sustainable economic and social development, as well as be a vital element for the conservation of ecosystems and the life of all beings on our planet. On the other hand, natural and anthropic disasters from floods and droughts may occur. The modeling of hydrological historical series has extensively been studied in the literature for important applications involving the water resources’ planning and management. There are several temporal series prediction’s techniques in the literature. Some of them are characterized as classical linear methods whose adjusts for multivariate or multi-input prediction problems can be difficult. On the other hand, artificial neural networks can learn complex nonlinear relationships from time series, and the deep learning model LSTM is considered the most successful type of recurrent neural network capable of directly supporting multivariate prediction problems. This work presents a comparison between two forecasting’s models of time series: ARIMA, a classical linear model, and an LSTM neural network, a nonlinear model. As a case study, we used the time series of four measurings’ substations of one of the very important Brazilian rivers - the Paraíba do Sul river. These time series are difficult to predict since their history series has flaws and high oscillation in the data. The LSTM, which is a robust model, performs better in analyzing the behavior of this type of time series.
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Governo do Brasil. https://www.brasil.gov.br/noticias/meio-ambiente. Accessed 27 Mar 2019
National water agency. https://www.ana.gov.br/. Accessed 21 Jun 2019
Abudu, S., Cui, C.I., King, J.P., Abudukadeer, K.: Comparison of performance of statistical models in forecasting monthly streamflow of Kizil river, China. Water Sci. Eng. 3(3), 269–281 (2010)
Asadi, S., Shahrabi, J., Abbaszadeh, P., Tabanmehr, S.: A new hybrid artificial neural networks for rainfall-runoff process modeling. Neurocomputing 121, 470–480 (2013)
Carelli, T.G., Plantz, J.B., Borghi, L.: Facies and paleoenvironments in paraíba do sul deltaic complex area, north of Rio de Janeiro state. Brazil. J. South American Earth Sci. 86, 431–446 (2018)
Carlisle, D.M., Falcone, J., Wolock, D.M., Meador, M.R., Norris, R.H.: Predicting the natural flow regime: models for assessing hydrological alteration in streams. River Res. Appl. 26(2), 118–136 (2010)
Caruana, R., Lawrence, S., Giles, C.L.: Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping. In: Advances in Neural Information Processing Systems, pp. 402–408 (2001)
George, E.P., Box, G.M.J.: Time Series Analysis: Forecasting and Control. Holden-Day Series in time series analysis and digital processing. Holden-Day, San Francisco (1976)
Gers, F., Schmidhuber, J., Cummins, F.: Learning to forget: continuous prediction with LSTM. Technical report, Technical Report IDSIA-01-99 (2000)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)
Graves, A., Jaitly, N., Mohamed, A.R.: Hybrid speech recognition with deep bidirectional LSTM. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 273–278. IEEE (2013)
Guimarãlise da previsibilidade de cheias na bacia do rio uruguai através do modelo mgb-iph (2018)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning - Data Mining, Inference, and Prediction, 2nd edn. Springer, New York (2009). https://doi.org/10.1007/BF02985802
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Jain, A., Sudheer, K., Srinivasulu, S.: Identification of physical processes inherent in artificial neural network rainfall runoff models. Hydrol. Process. 18(3), 571–581 (2004)
Kahn, J.R., Vásquez, W.F., de Rezende, C.E.: Choice modeling of system-wide or large scale environmental change in a developing country context: lessons from the Paraíba do Sul river. Sci. Total Environ. 598, 488–496 (2017)
Khair, A.F., Awang, M.K., Zakaraia, Z.A., Mazlan, M.: Daily streamflow prediction on time series forecasting. J. Theoret. Appl. Inf. Technol. 95(4), 804 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI 14, 1137–1145 (1995)
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., et al.: Rainfall-runoff modelling using long short-term memory (LSTM) networks (2018)
Krishna, B., Rao, Y.S., Nayak, P.: Time series modeling of river flow using wavelet neural networks. J. Water Resour. Prot. 3(01), 50 (2011)
Laptev, N., Yosinski, J., Li, L.E., Smyl, S.: Time-series extreme event forecasting with neural networks at uber. In: International Conference on Machine Learning, pp. 1–5, no. 34 (2017)
Miguens, F.C., de Oliveira, M.L., de Oliveira Ferreira, A., Barbosa, L.R., de Melo, E.J.T., de Carvalho, C.E.V.: Structural and elemental analysis of bottom sediments from the Paraíba do Sul River (SE, Brazil) by analytical microscopy. J. South American Earth Sci. 66, 82–96 (2016)
Mikolov, T., Karafiát, M., Burget, L., Černockỳ, J., Khudanpur, S.: Recurrent neural network based language model. In: Eleventh Annual Conference of the International Speech Communication Association (2010)
Ng, A., Katanforoosh, K., Mourri, Y.: Sequence models. Deeplearning. AI on Coursera (2018)
Patel, S.S., Ramachandran, P.: A comparison of machine learning techniques for modeling river flow time series: the case of upper cauvery river basin. Water Resour. Manag. 29(2), 589–602 (2015)
Pena, E.H.M., de Assis, M.V.O., Proença, M.L.: Anomaly detection using forecasting methods ARIMA and HWDS. In: 2013 32nd International Conference of the Chilean Computer Science Society (SCCC), pp. 63–66 (2013). https://doi.org/10.1109/SCCC.2013.18
Salomão, M., Molisani, M., Ovalle, A., Rezende, C., Lacerda, L., Carvalho, C.: Particulate heavy metal transport in the lower Paraíba do Sul river basin, Southeastern, Brazil. Hydrol. Process. 15(4), 587–593 (2001)
Shafaei, M., Kisi, O.: Predicting river daily flow using wavelet-artificial neural networks based on regression analyses in comparison with artificial neural networks and support vector machine models. Neural Comput. Appl. 28(1), 15–28 (2017)
da Silva, I.N., Cagnon, J.Â., Saggioro, N.J.: Recurrent neural network based approach for solving groundwater hydrology problems. In: Artificial Neural Networks-Architectures and Applications. IntechOpen (2013)
Sobral, B.S., et al.: Drought characterization for the state of Rio de Janeiro based on the annual SPI index: trends, statistical tests and its relation with ENSO. Atmos. Res. 220, 141–154 (2019)
Trento, A., Vinzón, S.: Experimental modelling of flocculation processes-the case of Paraiba do Sul Estuary. Int. J. Sedim. Res. 29(3), 378–390 (2014)
Valipour, M., Banihabib, M.E., Behbahani, S.M.R.: Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. J. Hydrol. 476, 433–441 (2013)
Vásquez, W.F., de Rezende, C.E.: Willingness to pay for the restoration of the Paraíba do Sul River: a contingent valuation study from Brazil. Ecohydrol. Hydrobiol. (2018)
Yaseen, Z.M., El-Shafie, A., Jaafar, O., Afan, H.A., Sayl, K.N.: Artificial intelligence based models for stream-flow forecasting: 2000–2015. J. Hydrol. 530, 829–844 (2015)
Zhang, J., Zhu, Y., Zhang, X., Ye, M., Yang, J.: Developing a long short-term memory (LSTM) based model for predicting water table depth in agricultural areas. J. Hydrol. 561, 918–929 (2018)
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Campos, L.C.D., Goliatt da Fonseca, L., Fonseca, T.L., de Abreu, G.D., Pires, L.F., Gorodetskaya, Y. (2019). Short-Term Streamflow Forecasting for Paraíba do Sul River Using Deep Learning. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_43
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DOI: https://doi.org/10.1007/978-3-030-30241-2_43
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