Deep Learning Based Anomaly Detection in Water Distribution Systems

Qian，Kai; Jiang，Jie; Ding，Yulong; Yang，Shuanghua

10.1109/ICNSC48988.2020.9238099

2020-10-30

978-1-7281-6856-2

2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

1-6

30 Oct.-2 Nov. 2020

Nanjing, China

Water distribution system (WDS) is one of the most essential infrastructures all over the world. However, incidents such as natural disasters, accidents and intentional damages are endangering the safety of drinking water. With the advance of sensor technologies, different kinds of sensors are being deployed to monitor operative and quality indicators such as flow rate, pH, turbidity, the amount of chlorine dioxide etc. This brings the possibility to detect anomalies in real time based on the data collected from the sensors and different kinds of methods have been applied to tackle this task such as the traditional machine learning methods (e.g. logistic regression, support vector machine, random forest). Recently, researchers tried to apply the deep learning methods (e.g. RNN, CNN) for WDS anomaly detection but the results are worse than that of the traditional machine learning methods. In this paper, by taking into account the characteristics of the WDS monitoring data, we integrate sequence-to-point learning and data balancing with the deep learning model Long Short-term Memory (LSTM) for the task of anomaly detection in WDSs. With a public data set, we show that by choosing an appropriate input length and balance the training data our approach achieves better F1 score than the state-of-the-art method in the literature.

anomaly detection deep learning sensor network sequence-to-point learning water quality

第一

英语

EI

20204709520861

Support vector machines ; Water quality ; Disasters ; Long short-term memory ; Water distribution systems ; Potable water ; Balancing ; Sensor networks ; Learning systems ; Decision trees

Water Resources:444 ; Water Analysis:445.2 ; Water Supply Systems:446.1 ; Mechanical Design:601 ; Computer Software, Data Handling and Applications:723 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Systems Science:961

2-s2.0-85096356332

Scopus

Southern University of Science and Technology,Department of Computer Science,Shenzhen,China

Qian，Kai,Jiang，Jie,Ding，Yulong,et al. Deep Learning Based Anomaly Detection in Water Distribution Systems[C],2020:1-6.