A hybrid machine learning approach for enhanced anomaly detection in drinking water quality

被引：0

作者：

Kalaivanan K. ^{[1
]}

Vellingiri J. ^{[1
]}

机构：

[1] School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore

来源：

International Journal of Environmental Studies | 2024年 / 81卷 / 02期

关键词：

Anomaly detection; autoencoder; extreme machine learning;

D O I：

10.1080/00207233.2024.2313349

中图分类号：

学科分类号：

摘要：

Machine learning has become important for anomaly detection in water quality prediction. Data anomalies are often caused by the difficulties of analysing large amounts of data, both technical and human, but approaches have been inadequate when water contamination increases. The paper reports an anomaly detection technique based on an autoencoder and an extreme learning machine (AEELM). This approach uses an autoencoder for feature selection and an ELM algorithm for classification purposes. It is estimated using the Cauvery River dataset. The autoencoder-based ELM techniques outperformed other models with 83% accuracy, 77% precision, 83% recall, and 83% F1-level characteristic performance indicators. These results demonstrate its great effectiveness. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

引用

页码：661 / 674

页数：13

共 28 条

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