宋麗娜1，劉 淼2，秦 韜3，何 鑫3，郭中磊2，王小勝1

　　(1.河北工程大學數理科學與工程學院，河北 邯鄲 056038;

　　2.河北省水資源研究與水利技術試驗推廣中心，河北 石家莊 050000;

　　3.中國水利水電科學研究院水資源研究所，北京 100038)

　　摘 要：為有效識別城鎮取用水監測數據異常值，提高數據的可靠性與真實性，通過結合局部異常因子(LOF)算法與互補總體經驗模態分解(CEEMD)法，開發城鎮取用水監測數據異常值自動識別的方法。先應用LOF進行可直觀異常值識別，再應用CEEMD對修正后的數據序列進行頻譜分解，通過低頻疊加分量擬合序列并設定相對誤差閾值用以識別不可直觀異常值，并以河北省某自來水廠日取用水監測數據進行實驗分析，結果顯示，修正后的年取用水數據由直接監測的51.27萬m3減少為41.14萬m3，修正結果與人工核定的年取用水量更為接近。研究結果表明：直接使用監測數據用以統計年取用水量存在較大誤差，提出的方法可以有效識別取用水量監測數據中的異常值并進行修正，為后續的水資源強監管提供技術支撐。

　　關鍵詞：監測數據;異常值;LOF;CEEMD;城鎮取用水

　　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