Abstract：Water resources are related to national economy and people’s livelihood, detection of water quality anomaly has attracted more attention because of the water pollution events happened in recent years. In this paper, the detection method of water quality anomaly with UV-Vis Spectrum based on supervised learning was proposed to solve the problems of existing methods, which behaved as a high detection limit and poor adaptability method. The pretreatment of orthogonal projection was used to correct the gap between different batches of spectral data. Afterwards the Partial Least Squares Discriminatory Analysis was adopted to extract the features from the data set. Outliers were found by comparing the alarm signal with the best threshold from the training set. Finally, Sequential Bayesian Method was used to update the probability of Contaminate Intrusion Events and to get the alarm sequence. The results showed that the proposed method had the lower detection limit than unsupervised method and the pretreatment of orthogonal projection improved the adaptability of detection method based on supervised learning for baseline changing.
Key words：UV-Vis spectrum; Water quality anomaly detection; Orthogonal projection; Supervised learning; Partial least squares discriminatory analysis
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