Li Qingbo, Shao Xupeng, Cui Houxin, Wei Yuan, Shang Yongchang
Precision Opto-Mechatronics Technology Key Laboratory of Education Ministry, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China.
Research and Development Department, Hebei Sailhero Environmental Protection Hi-Tech Co., Ltd, Shijiazhuang, China.
Appl Spectrosc. 2023 Dec;77(12):1371-1381. doi: 10.1177/00037028231206191.
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.
地表水的污染危害极大。紫外可见（UV-Vis）光谱法是检测水污染的一种有效方法。然而，地表水水质会受到降雨、水流变化等引起的水文波动影响，导致光谱特征随时间发生变化。在污染检测过程中，这种变化会造成水文波动光谱与污染水体光谱之间的混淆，从而增加误报率。此外，当信噪比很低时，污染水体漏报是一个常见问题。本文提出了一种动态多变量离群采样率检测（DM-SRD）算法。引入了一种动态更新策略以增强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。利用从实际地表水站点采集的光谱进行了两个实验，并构建了水文波动情况。为验证DM-SRD方法的有效性，将其与静态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显著降低了误报率并避免了漏报。此外，结果表明无论数据库中是否包含水文波动的先验信息，DM-SRD都能保持较高的检测准确率，这表明其具有很强的适应性和鲁棒性。
