论文标题
污水管网络的早期异常检测：各种异常检测算法的包装
Early Abnormal Detection of Sewage Pipe Network: Bagging of Various Abnormal Detection Algorithms
论文作者
论文摘要
污水管道网络的异常会影响整个城市的正常运作。因此，重要的是早期检测异常。本文提出了一种早期异常检测方法。 The abnormalities are detected by using the conventional algorithms, such as isolation forest algorithm, two innovations are given: (1) The current and historical data measured by the sensors placed in the sewage pipe network (such as ultrasonic Doppler flowmeter) are taken as the overall dataset, and then the general dataset is detected by using the conventional anomaly detection method to diagnose the anomaly of the data.异常是指与整个数据集中其他样本不同的样本。由于异常的定义不是通过算法，而是整个数据集，因此整个数据集的构造是提出早期异常检测算法的关键。 （2）提出了各种常规异常检测算法的装袋策略，以实现具有高精度和召回率的早期检测。结果表明，该方法可以达到早期的异常检测，最高精度为98.21％，召回率为63.58％，F1得分为0.774。
Abnormalities of the sewage pipe network will affect the normal operation of the whole city. Therefore, it is important to detect the abnormalities early. This paper propose an early abnormal-detection method. The abnormalities are detected by using the conventional algorithms, such as isolation forest algorithm, two innovations are given: (1) The current and historical data measured by the sensors placed in the sewage pipe network (such as ultrasonic Doppler flowmeter) are taken as the overall dataset, and then the general dataset is detected by using the conventional anomaly detection method to diagnose the anomaly of the data. The anomaly refers to the sample different from the others samples in the whole dataset. Because the definition of anomaly is not through the algorithm, but the whole dataset, the construction of the whole dataset is the key to propose the early abnormal-detection algorithms. (2) A bagging strategy for a variety of conventional anomaly detection algorithms is proposed to achieve the early detection of anomalies with the high precision and recall. The results show that this method can achieve the early anomaly detection with the highest precision of 98.21%, the recall rate 63.58% and F1-score of 0.774.
