Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/79446

TitleEvaluating unidimensional convolutional neural networks to forecast the influent pH of wastewater treatment plants
Author(s)Oliveira, Pedro
Fernandes, B.
Aguiar, Francisco
Pereira, M. A.
Novais, Paulo
KeywordsConvolutional Neural Networks
Deep Learning
Influent pH
Time series
Wastewater Treatment Plants
Issue date2021
PublisherSpringer, Cham
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
CitationOliveira, P., Fernandes, B., Aguiar, F., Pereira, M.A., Novais, P. (2021). Evaluating Unidimensional Convolutional Neural Networks to Forecast the Influent pH of Wastewater Treatment Plants. In: , et al. Intelligent Data Engineering and Automated Learning – IDEAL 2021. IDEAL 2021. Lecture Notes in Computer Science(), vol 13113. Springer, Cham. https://doi.org/10.1007/978-3-030-91608-4_44
Abstract(s)One of our society’s challenges today is water resources management due to its importance for human life. The monitoring of various substances present in wastewater is a crucial part of the process of Wastewater Treatment Plants (WWTPs). One of these substances is the influent’s pH, which plays a fundamental role in the nitrification and nitration processes. Hence, this paper presents a study to forecast the influent pH in a WWTP for the next two days. For this purpose, several candidate models were conceived, tunned and evaluated, taking into account the one-dimensional Convolutional Neural Networks (CNNs) considering two distinct approaches in the Pooling layer: the channels’ last and the channels’ first. The best candidate model obtained a Mean Absolute Error (MAE) of 0.257, following the channel’s last approach, compared to the channels’ first that obtained a MAE of 0.272.
TypeConference paper
URIhttps://hdl.handle.net/1822/79446
ISBN978-3-030-91607-7
e-ISBN978-3-030-91608-4
DOI10.1007/978-3-030-91608-4_44
ISSN0302-9743
Publisher versionhttps://link.springer.com/chapter/10.1007/978-3-030-91608-4_44
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

Files in This Item:
File Description SizeFormat 
IDEAL_2021_paper_71.pdf
  Restricted access
297,94 kBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID