Applying Tree Ensemble to Detect Anomalies in Real-World Water Composition Dataset

Abstract

Drinking water is one of fundamental human needs. During delivery in distribution network, drinking water is susceptible to contaminants. Early recognition of changes in water quality is essential in the provision of clean and safe drinking water. For this purpose, Contamination warning system (CWS) composed of sensors, central database and event detection system (EDS) has been developed. Conventionally, EDS employs time series analysis and domain knowledge for automated detection. This paper proposes a general data driven approach to construct an automated online event detention system for drinking water. Various tree ensemble models are investigated in application to real-world water quality data. In particular, gradient boosting methods are shown to overcome challenges in time series data imbalanced class and collinearity and yield satisfied predictive performance.