Spatio-Temporal Anomaly Detection in Groundwater Electrical Conductivity Using a Hybrid Framework of Isolation Forest and Autoencoder

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.