With the rapid development of monitoring systems, extensive amount of water quality high-resolution measurements are accumulated, which make it unrealistic to manually extract the water quality anomaly features from the huge river environment information. In this study, a hybrid anomaly detection framework is developed by the combination of prediction-based and classification-based data-driven methods to provide a scientific indication for river pollution identification. A Variational Mode Decomposition-Back Propagation Neural Network (VMD-BPNN) model is used to analyze the real-time water quality variation tendencies in the first stage. Additionally, a Support Vector Data Description (SVDD) algorithm is adopted to capture the multi-dimensional water quality anomaly characteristics in the second stage. The developed hybrid framework is then applied to the Kansas River in America, to verify its river pollution identification performance in comparison to different anomaly detection methods and in various anomaly-level scenarios. The developed hybrid framework can achieve a maximum Area Under the Curve (AUC) value of 0.932 under a two-dimensional anomaly detection pattern with the True Positive Rate (TPR) and False Positive Rate (FPR) values of 0.861 and 0.142, respectively. The results indicate that the developed hybrid framework can provide an effective river pollution identification performance with dynamically determined warning thresholds. Meanwhile, a vigorous anomaly detection pattern can improve the pollution identification performance by considering the cumulative interactions among the multi-dimensional water quality parameters.

Journal of Cleaner Production, 467

ISSN:0959-6526