The contamination of surface water is of great harm. Ultraviolet-visible (UV-Vis) spectroscopy is an effective method to detect water contamination. However, surface water quality is influenced by hydrological fluctuation caused by rain, change of flow, etc., leading to changes of spectral characteristics over time. In the process of contamination detection, such changes cause confusion between hydrological fluctuation spectra and contaminated water spectra, thus increasing the false alarm rate. Besides, missing alarms of contaminated water is a common problem when the signal-to-noise ratio is low. In this paper, a dynamic multivariable outlier sampling rate detection (DM-SRD) algorithm is proposed. A dynamic updating strategy is introduced to increase adaptability to hydrological fluctuation. Additionally, multiple outlier variables are adopted as outlying degree indicators, which increases the accuracy of contamination detection. Two experiments were carried out using spectra collected from real surface water sites and hydrological fluctuation was constructed. To verify the effectiveness of the DM-SRD method, a comparison with the static SRD method and spectral match method was conducted. The results show that the accuracy of the DM-SRD method is 97.8%. Compared with the other two detection methods, DM-SRD significantly reduces false alarm rate and avoids missing alarms. Additionally, the results demonstrate that whether the database contained prior information on hydrological fluctuation or not, DM-SRD maintained high detection accuracy, which indicates great adaptability and robustness.

中文翻译：

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使用紫外可见光谱的动态多元离群值检测算法实时监测水文波动的地表水污染。

地表水的污染危害极大。紫外可见（UV-Vis）光谱是检测水污染的有效方法。然而，地表水质受降雨、流量变化等水文波动的影响，导致光谱特征随时间的变化。在污染检测过程中，这种变化会导致水文波动谱与污染水谱之间的混淆，从而增加误报率。此外，当信噪比较低时，污染水漏报警也是一个常见问题。本文提出了一种动态多变量异常采样率检测（DM-SRD）算法。引入动态更新策略以提高对水文波动的适应性。此外，采用多个离群变量作为离群程度指标，提高了污染检测的准确性。利用从真实地表水地点收集的光谱进行了两个实验，并构建了水文波动。为了验证DM-SRD方法的有效性，与静态SRD方法和光谱匹配方法进行了比较。结果表明，DM-SRD方法的准确率为97.8%。与其他两种检测方法相比，DM-SRD显着降低了误报率，避免了漏报。此外，结果表明，无论数据库是否包含水文波动先验信息，DM-SRD都保持较高的检测精度，具有很强的适应性和鲁棒性。