Abstract
For the management and operation of a Wastewater Treatment Plant (WWTP), the influent flow is one of the most important variables. Hence, this paper presents an evaluation of multiple Deep Learning models to forecast the influent flow in WWTPs for the next three days, taking into account previous influent observations as well as historical climatological data. Long Short-Term Memory networks (LSTMs) and one-dimensional Convolutional Neural Networks (CNNs), following a channels’ last approach, were conceived to tackle this time series problem. The best candidate LSTM model was able to forecast the influent flow with an approximate overall error of 200 \(\mathrm{m}^3\) for the three forecast days. On the other hand, the best candidate CNN model presented a slightly higher error, being outperformed by LSTM-based models. Nonetheless, CNNs, which are typically applied in the computer vision domain, also showed interesting performance for time series forecasting.
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Baus, D.: Overpopulation and the Impact on the Environment (2017)
Metcalf, L., Eddy, H.P., Tchobanoglous, G.: Wastewater Engineering: Treatment, Disposal, and Reuse, vol. 4. McGraw-Hill, New York (1979)
Di Fraia, S., Massarotti, N., Vanoli, L.: A novel energy assessment of urban wastewater treatment plants. Energy Conversion Manag. 163, 304–313 (2018). https://doi.org/10.1016/j.enconman.2018.02.058
Zhang, D., Martinez, N., Lindholm, G., Ratnaweera, H.: Manage sewer in-line storage control using hydraulic model and recurrent neural network. Water Resources Manag. 32(6), 2079–2098 (2018). https://doi.org/10.1007/s11269-018-1919-3
Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179–211 (1990). https://doi.org/10.1207/s15516709cog1402_1
Siegelmann, H.T., Horne, B.G., Giles, C.L.: Computational capabilities of recurrent NARX neural networks. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 27(2), 208–215 (1997). https://doi.org/10.1109/3477.558801
Zhou, P., Li, Z., Snowling, S., Baetz, B.W., Na, D., Boyd, G.: A random forest model for inflow prediction at wastewater treatment plants. Stochastic Environ. Res. Risk Assess. 33(10), 1781–1792 (2019). https://doi.org/10.1007/s00477-019-01732-9
Szelag, B., Bartkiewicz, L., Studziński, J., Barbusiński, K.: Evaluation of the impact of explanatory variables on the accuracy of prediction of daily inflow to the sewage treatment plant by selected models nonlinear. Arch. Environ. Protect. 43(3), 74–81 (2017). https://doi.org/10.1515/aep-2017-0030
Fernandes, B., Silva, F., Alaiz-Moretón, H., Novais, P., Analide, C., Neves, J.: Traffic flow forecasting on data-scarce environments using ARIMA and LSTM networks. In: Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S. (eds.) WorldCIST’19 2019. AISC, vol. 930, pp. 273–282. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16181-1_26
Sagheer, A., Kotb, M.: Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing 323, 203–213 (2019). https://doi.org/10.1016/j.neucom.2018.09.082
DiPietro, R., Hager, G.D.: Deep learning: RNNs and LSTM. In: Handbook of Medical Image Computing and Computer Assisted Intervention, pp. 503–519. Academic Press (2020). https://doi.org/10.1016/B978-0-12-816176-0.00026-0
LeCun, Y., et al.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems, pp. 396–404 (1990)
Zhao, B., Lu, H., Chen, S., Liu, J., Wu, D.: Convolutional neural networks for time series classification. J. Syst. Eng. Electron. 28(1), 162–169 (2017). https://doi.org/10.21629/JSEE.2017.01.18
Borovykh, A., Bohte, S., Oosterlee, C.W.: Dilated convolutional neural networks for time series forecasting. J. Comput. Finance (2018, Forthcoming)
Koprinska, I., Wu, D., Wang, Z.: Convolutional neural networks for energy time series forecasting. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2018). https://doi.org/10.1109/IJCNN.2018.8489399
Carneiro, D., Novais, P., Pêgo, J.M., Sousa, N., Neves, J.: Using mouse dynamics to assess stress during online exams. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds.) HAIS 2015. LNCS (LNAI), vol. 9121, pp. 345–356. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19644-2_29
Costa, A., Rincon, J.A., Carrascosa, C., Julian, V., Novais, P.: Emotions detection on an ambient intelligent system using wearable devices. Future Gen. Comput. Syst. 92, 479–489 (2019)
Lima, L., Novais, P., Costa, R., Cruz, J.B., Neves, J.: Group decision making and Quality-of-Information in e-Health systems. Logic J. IGPL 19(2), 315–332 (2011)
Acknowledgments
This work is financed by National Funds through the Portuguese funding agency, FCT - Foundation for Science and Technology within project DSAIPA/AI/0099/2019. The work of Bruno Fernandes is also supported by a Portuguese doctoral grant, SFRH/BD/130125/2017, issued by FCT.
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Oliveira, P., Fernandes, B., Aguiar, F., Pereira, M.A., Analide, C., Novais, P. (2020). A Deep Learning Approach to Forecast the Influent Flow in Wastewater Treatment Plants. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_32
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