Online anomaly detection for drinking water quality using a multi-objective machine learning approach
Abstract
This document proposes the use of multi-objective machine learning in order to solve the problem of online anomaly detection for drinking water quality. Such problem consists of an imbalanced data set where events, the minority class, must be correctly detected based on a time series denoting water quality data and operative data. In order to develop two different robust systems, signal processing and feature engineering are used to prepare the data, while evolutionary multi-objective optimization is used for feature selection and ensemble generation. The proposed systems are tested with hold-out validation during optimization, and are expected to generalize well the predictions for future testing data.
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