Early warning of water pollution incidents based on abnormal change of water quality data from high frequency online monitoring

With the high frequency automatic monitoring of surface water quality, a technique for early warning of water pollution incidents was developed using the water quality soft measurement and abnormal detection of time series. This technique takes the assumption that water pollution incidents would cause the change of typical automatic monitoring water quality parameters, and then establishes the linear relationship between the water quality parameters and online high frequency monitoring water quality parameters. Using the artificial neural network, the change of water quality parameters in a short duration was predicted; using the time series of residual error, the threshold of abnormal change was determined. Finally, early warning of pollution incidents could be achieved through detecting abnormal change based on sequential leader clustering algorithm. To verify the technique, this study takes the online monitoring data obtained from the Potomac River in Virginia, USA as a case study. The analysis of the receiver operating characteristic curve (ROC) shows that the detection accuracies of double and triple abnormal levels can reach 62.7% and 92.5%, respectively. Because the concentration level of a water pollution incident is usually significantly higher than 3times, this technique can provide a relative high accurate early warning. Compared with traditional abnormal detection methods, this technique can shorten the detection time. Along with increasing improvement of automatic monitoring facilities, this study provided a new avenue for early warning of, and prompt response to, pollution incidents.