We develop an approach for water quality time series monitoring and contamination event detection.
The approach combines affine projection algorithms and an autoregressive (AR) model to predict
water quality time series. Then, we apply online change-point detection methods to the estimated
residuals to determine the presence, or not, of contamination events. Particularly, we compare the
performance of four change-point detection methods, namely, sequential probability ratio test
(SPRT), cumulative sum (CUSUM), binomial event discriminator (BED), and online Bayesian changepoint
detection (OBCPD), by using residuals obtained from four water quality time series, chlorine,
conductivity, total organic carbon, and turbidity. Our fundamental criterion for the performance
evaluation of the four change-point detection methods is given by the receiver operating
characteristic (ROC) curve which is characterized by the true positive rate as a function of the false
positive rate. We highlight with detailed experiments that OBCPD provides the best performance for
large contamination events, and we also provide insight on the choice of change-point detection
algorithms to consider for designing efficient contamination detection schemes.
Key words | change-point detection, prediction, ROC, time series, water quality