Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/79449
Título: | Unsupervised learning approach for pH anomaly detection in wastewater treatment plants |
Autor(es): | Gigante, Diogo Oliveira, Pedro Fernandes, B. Lopes, Frederico Novais, Paulo |
Palavras-chave: | Anomaly detection Isolation Forest One-Class Support Vector Machine pH 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: | Gigante, D., Oliveira, P., Fernandes, B., Lopes, F., Novais, P. (2021). Unsupervised Learning Approach for pH Anomaly Detection in Wastewater Treatment Plants. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2021. Lecture Notes in Computer Science(), vol 12886. Springer, Cham. https://doi.org/10.1007/978-3-030-86271-8_49 |
Resumo(s): | Sustainability has been a concern for society over the past few decades, preserving natural resources being one of the main themes. Among the various natural resources, water was one of them. The treatment of residual waters for future reuse and release to the environment is a fundamental task performed by Wastewater Treatment Plants (WWTP). Hence, to guarantee the quality of the treated effluent in a WWTP, continuous control and monitoring of abnormal events in the substances present in this water resource are necessary. One of the most critical substances is the pH that represents the measurement of the hydrogen ion activity. Therefore, this work presents an approach with a conception, tune and evaluation of several candidate models, based on two Machine Learning algorithms, namely Isolation Forests (iF) and One-Class Support Vector Machines (OCSVM), to detect anomalies in the pH on the effluent of a multi-municipal WWTP. The OCSVM-based model presents better performance than iF-based with an approximate 0.884 of Area Under The Curve - Receiver Operating Characteristics (AUC-ROC). |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/79449 |
ISBN: | 978-3-030-86270-1 |
e-ISBN: | 978-3-030-86271-8 |
DOI: | 10.1007/978-3-030-86271-8_49 |
ISSN: | 0302-9743 |
Versão da editora: | https://link.springer.com/chapter/10.1007/978-3-030-86271-8_49 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
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Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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HAIS_2021_paper_53.pdf Acesso restrito! | 323,25 kB | Adobe PDF | Ver/Abrir |