A Review of Machine Learning-Based Time-Series Anomaly Detection in the Water Domain

Anomaly detection in water-related time-series data often reveals important environmental problems and serves as a starting point for scientific discoveries. Machine learning has become the mainstream method and a research hotspot for anomaly detection in recent years. This review examines 106 research articles from the Web of Science database published over the past 10 years. Unlike other surveys, this review focuses on anomalies arising from the water-related variables themselves rather than from equipment malfunctions. The work assesses the overall trends in the application and development of machine learning models for water-related anomaly detection. It classifies machine learning-based anomaly-detection models from two dimensions: development stage and anomaly-detection paradigm. Our analysis covers the mechanisms, strengths, limitations, and applications of various machine learning-based anomaly-detection models across different paradigms, highlighting current challenges and prospective research directions in water-related anomaly detection.