宋丽娜1，刘 淼2，秦 韬3，何 鑫3，郭中磊2，王小胜1

　　(1.河北工程大学数理科学与工程学院，河北 邯郸 056038;

　　2.河北省水资源研究与水利技术试验推广中心，河北 石家庄 050000;

　　3.中国水利水电科学研究院水资源研究所，北京 100038)

　　摘 要：为有效识别城镇取用水监测数据异常值，提高数据的可靠性与真实性，通过结合局部异常因子(LOF)算法与互补总体经验模态分解(CEEMD)法，开发城镇取用水监测数据异常值自动识别的方法。先应用LOF进行可直观异常值识别，再应用CEEMD对修正后的数据序列进行频谱分解，通过低频叠加分量拟合序列并设定相对误差阈值用以识别不可直观异常值，并以河北省某自来水厂日取用水监测数据进行实验分析，结果显示，修正后的年取用水数据由直接监测的51.27万m3减少为41.14万m3，修正结果与人工核定的年取用水量更为接近。研究结果表明：直接使用监测数据用以统计年取用水量存在较大误差，提出的方法可以有效识别取用水量监测数据中的异常值并进行修正，为后续的水资源强监管提供技术支撑。

　　关键词：监测数据;异常值;LOF;CEEMD;城镇取用水

　　Outlier identification of urban water intake monitoring data based on LOF and CEEMD

　　SONG Li'na1，LIU Miao2，QIN Tao3，HE Xin3，GUO Zhonglei2，WANG Xiaosheng1

　　(1.School of Mathematics and Physics，Hebei University of Engineering，Handan056038，China;

　　2. Center of Water Resources Research and Water Techniques Testing & Dissemination of Hebei Province，Shijiazhuang 050000，China;

　　3. Department of Water Resources，China Institute of Water Resources and Hydropower Research，Beijing 100038，China)

　　Abstract：In order to identify the outliers of urban water intake monitoring data effectively and improve the reliability of the data, the automatic outlier identification method is developed by combining the Local Outlier Factor (LOF) method with the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method. LOF is used to identify observable outliers firstly and then CEEMD is applied for spectral decomposition of the revised data series. Sequences are fitted by low-frequency superposition components, and the relative error threshold is set to identify non-observable outliers. Taking the monitoring data of daily water intake of a waterworks in Hebei Province for experimental analysis, and the results show that the revised annual water intake data reduces from 512 700 m3 to 411 400 m3. The revised data is much closer to the manually approved annual data. And therefore there is a large error if the monitoring data was used directly to calculate the total annual water intake and consumption. The proposed method can effectively identify and correct the outliers in the urban water intake monitoring data, and provide technical support for the follow-up strong supervision of water resources.

　　Key words：monitoring data;outliers; LOF; CEEMD;urban water intake