Spatio-Temporal Anomaly Detection in Groundwater Electrical Conductivity Using a Hybrid Framework of Isolation Forest and Autoencoder
- 1 Branksome Hall Asia, 234 Global Rdu-Ro, Daejeong-Eup, Seogwipo-Si, Jeju-do, Korea
- 2 Seoul International School, 15 Seongnam-Daero, Gyeonggi-do, Korea
- 3 Korean International School- Pangyo Campus, Gyeonggi-do, Korea
- 4 Department of Computer Science and Engineering, Institute of Engineering and Management, Newtown, University of Engineering and Management, Action Area-III, Newtown Road, Kolkata, India
Abstract
Monitoring groundwater quality is vital for environmental safety and resource sustainability. This study combines Isolation Forest and Autoencoder models to detect anomalies in Electrical Conductivity (EC) and temperature, using monthly data collected in South Korea between 2006 and 2023. Linear regression and the Mann-Kendall test reveal a weak, episodic downward EC trend. Seasonal decomposition indicates annual cyclicality, while residual analysis uncovers localized anomalies. K-means clustering differentiates normal and contaminated groundwater patterns. The results highlight the effectiveness of integrating statistical and machine learning approaches for interpretable, data-driven groundwater quality monitoring in data-scarce environments.
DOI: https://doi.org/10.3844/jcssp.2026.273.383
Copyright: © 2026 Eunji Lee, Seunghyun Lim, Seojun Lee and Abhijit Debnath. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Groundwater Quality Monitoring
- Anomaly Detection
- Unsupervised Learning
- Seasonal Decomposition
- Hybrid Machine Learning Models