Skip to yearly menu bar Skip to main content


Poster
in
Affinity Workshop: New In ML

A One-Class Anomaly Detection Framework for Groundwater Quality Assessment


Abstract:

Assessing groundwater quality is crucial for public health but is challenged by the complexity of contaminants and the scarcity of labeled data. Traditional methods often require well-defined classes for both normal and anomalous samples, a condition rarely met in practice. This paper introduces a novel evaluation framework using one-class anomaly detection, trained exclusively on a small set of expert-identified, high-quality groundwater samples. We construct an ensemble of One-Class SVM, Isolation Forest, and Local Outlier Factor models and propose a new standard that classifies unseen samples into multiple levels of deviation based on the score distribution of the positive training data. Our ensemble model, validated against expert evaluations on a real-world hydrochemical dataset, demonstrates high accuracy. This provides a robust, nuanced, and practical solution for groundwater assessment in data-scarce environments, effectively establishing a new data-driven benchmark for water quality.

Chat is not available.