基于三维荧光时间序列双阈值的饮用水污染事件检测方法研究

A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy

目前,三维荧光技术在应急性饮用水污染事件检测中的应用越来越广泛,但其仍存在易受水环境波动影响、低浓度污染事件检出率较低等不足。因此针对在线检测需求,提出了一种基于时间序列双阈值的三维荧光饮用水异常事件检测方法。该方法采用主成分分析法提取检测样本的三维荧光光谱主元特征值,进行线性自回归(AR)模型训练并对未来时段水质样本主元特征值进行预测,通过与实测样本主元特征值作差得到特征值差值,同时结合实测特征值的变化率,设置特征值差值-特征值变化率双阈值,最终确定污染事件的时间起始点与结束点,从而确定整个污染事件。研究通过模拟高浓度污染事件、低浓度污染事件、供水水质波动等场景对所提方法进行了验证。实验结果表明,该方法不仅保持了高浓度污染事件检测的准确性,在检测低浓度污染、高干扰环境下的低浓度污染时,该方法相较于常规判别方法,检测结果准确率分别提高了9.4%和20.7%。

Three-dimensional fluorescence technology is attracting attention in detecting emergency drinking water pollution events.However,some unsolved problems remain,such as being easily affected by water environment fluctuations,low detection rate facing low-concentration organic pollutants,etc.Therefore,in response to the demand for online monitoring,this study proposed a time series double thresholds method for anomaly detection in drinking water using three-dimensional fluorescence.This method applied principal component analysis(PCA)to extract the feature spectrum of the detected samples and trained the linear autoregressive(AR)model to predict the principal component of the water samples in the future.The eigenvalue difference was then obtained by comparing the predicted and measured ones.At the same time,combined with the change rate of the measured eigenvalues,the double threshold for time series was set to finally determine the start and end points of the pollution event to determine the entire pollution event.The research validated the proposed method by simulating high-concentration pollution events,low-concentration pollution events,and fluctuations in water background.The experimental results show that this method maintains the detection accuracy for high-concentration pollution events.Moreover,compared with conventional methods,the proposed method improved the detection performance in low-concentration pollution events and low-concentration pollution in high-interference environments.The detection accuracy is increased by 9.4%and 20.7%,respectively.