Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/79446
Título: | Evaluating unidimensional convolutional neural networks to forecast the influent pH of wastewater treatment plants |
Autor(es): | Oliveira, Pedro Fernandes, B. Aguiar, Francisco Pereira, M. A. Novais, Paulo |
Palavras-chave: | Convolutional Neural Networks Deep Learning Influent pH Time series Wastewater Treatment Plants |
Data: | 2021 |
Editora: | Springer, Cham |
Revista: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Citação: | Oliveira, 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 |
Resumo(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. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/79446 |
ISBN: | 978-3-030-91607-7 |
e-ISBN: | 978-3-030-91608-4 |
DOI: | 10.1007/978-3-030-91608-4_44 |
ISSN: | 0302-9743 |
Versão da editora: | https://link.springer.com/chapter/10.1007/978-3-030-91608-4_44 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
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IDEAL_2021_paper_71.pdf Acesso restrito! | 297,94 kB | Adobe PDF | Ver/Abrir |