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TitleA deep learning approach to forecast the influent flow in wastewater treatment plants
Author(s)Oliveira, Pedro
Fernandes, Bruno
Aguiar, Francisco
Pereira, M. A.
Analide, Cesar
Novais, Paulo
KeywordsDeep Learning
Wastewater Treatment Plants
Influent flow
Long Short-Term Memory Networks
Convolutional Neural Networks
Issue date2020
PublisherSpringer Verlag
JournalLecture Notes in Computer Science
CitationOliveira 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.
Abstract(s)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 m3 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.
TypeConference paper
Publisher version
AccessRestricted access (UMinho)
Appears in Collections:CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series

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