Early Abnormal Detection of Sewage Pipe Network: Bagging of Various Abnormal Detection Algorithms

TL;DR

This paper introduces a bagging-based ensemble method combining various anomaly detection algorithms to enable early and precise detection of sewage pipe network abnormalities using sensor data.

Contribution

It proposes a novel bagging strategy that integrates multiple conventional anomaly detection algorithms for early sewage pipe network abnormality detection.

Findings

Achieved up to 98.21% precision in early anomaly detection

Attained a recall rate of 63.58%

F1-score reached 0.774

Abstract

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.