Application of machine learning techniques to predict anomalies in water supply networks

2016 ◽  
Vol 16 (6) ◽  
pp. 1528-1535 ◽  
Author(s):  
D. Vries ◽  
B. van den Akker ◽  
E. Vonk ◽  
W. de Jong ◽  
J. van Summeren
Keyword(s):  
Machine Learning ◽  
Water Supply ◽  
Data Streams ◽  
Hidden Variables ◽  
Streaming Data ◽  
Support Vector ◽  
Supply Network ◽  
Orthogonal Projections ◽  
Time Pattern ◽  
Water Supply Network

Methods to improve the operational efficiency of a water supply network by early detection of anomalies are investigated by making use of the data streams from multiple sensor locations within the network. The water supply network is a demonstration site of Vitens, a Dutch water company that has several district metering areas where flow, pressure, electrical conductance and temperature are measured and logged online. Three different machine learning approaches are tested for their feasibility to detect anomalies. In the first approach, day-dependent support vector regression (SVR) models are trained for predicting the measurement signals and compared to straightforward models using mean and median estimates, respectively. Using SVRs or the averaged data as real-time pattern recognizers on all available signals, large leakages can be detected. The second approach utilizes adaptive orthogonal projections and reports an event when the number of hidden variables required to describe the streaming data to a user-defined degree (energy-level threshold) increases. As a third approach, (unsupervised) clustering techniques are applied to detect anomalies and underlying patterns from the raw data streams. Preliminary results indicate that the current dataset is too limited in the amount of events and patterns to harness the potential of these techniques.

Pressure prediction and abnormal working conditions detection of water supply network based on LSTM

2020 ◽  
Vol 20 (3) ◽  
pp. 963-974 ◽  
Author(s):  
Zhe Xu ◽  
Zhihao Ying ◽  
Yuquan Li ◽  
Bishi He ◽  
Yun Chen
Keyword(s):  
Water Supply ◽  
Short Term Memory ◽  
Learning Performance ◽  
Support Vector ◽  
Supply Network ◽  
Short Term ◽  
Water Supply Network ◽  
Vector Machines ◽  
Long Short Term Memory ◽  
Deep Learning Model

Abstract In this study, a deep learning model based on LSTM (Long Short-Term Memory) is used to predict the state of a water supply network due to its highly complex nonlinearity. The inputs of the model include state information on the pressures at measuring points, as well as control information on the water supply pressure and flow at each entry point. In order to enhance the performance of the model in feature extraction and identification and improve prediction accuracy, a parallel LSTM tandem DNN deep neural network model (PLDNN) is proposed. The experimental results indicate that the model has better learning performance and accuracy compared with traditional prediction methods (artificial neural networks, support vector machines, etc.) and general LSTM models.

scholarly journals Machine learning model and strategy for fast and accurate detection of leaks in water supply network

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Xudong Fan ◽  
Xijin Zhang ◽  
Xiong ( Bill) Yu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Water Supply ◽  
Water Pressure ◽  
Leak Detection ◽  
Supply Network ◽  
Simulation Code ◽  
Water Supply Network ◽  
Detection Strategy ◽  
Monitoring Area

AbstractThe water supply network (WSN) is subjected to leaks that compromise its service to the communities, which, however, is challenging to identify with conventional approaches before the consequences surface. This study developed Machine Learning (ML) models to detect leaks in the WDN. Water pressure data under leaking versus non-leaking conditions were generated with holistic WSN simulation code EPANET considering factors such as the fluctuating user demands, data noise, and the extent of leaks, etc. The results indicate that Artificial Neural Network (ANN), a supervised ML model, can accurately classify leaking versus non-leaking conditions; it, however, requires balanced dataset under both leaking and non-leaking conditions, which is difficult for a real WSN that mostly operate under normal service condition. Autoencoder neural network (AE), an unsupervised ML model, is further developed to detect leak with unbalanced data. The results show AE ML model achieved high accuracy when leaks occur in pipes inside the sensor monitoring area, while the accuracy is compromised otherwise. This observation will provide guidelines to deploy monitoring sensors to cover the desired monitoring area. A novel strategy is proposed based on multiple independent detection attempts to further increase the reliability of leak detection by the AE and is found to significantly reduce the probability of false alarm. The trained AE model and leak detection strategy is further tested on a testbed WSN and achieved promising results. The ML model and leak detection strategy can be readily deployed for in-service WSNs using data obtained with internet-of-things (IoTs) technologies such as smart meters.

scholarly journals Machine Learning Model and Strategy for Fast and Accurate Detection of Leaks in Water Supply Network

2021 ◽  
Author(s):  
Xudong Fan ◽  
Xijin Zhang ◽  
Xiong (Bill) Yu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Water Supply ◽  
Water Pressure ◽  
Leak Detection ◽  
Supply Network ◽  
Simulation Code ◽  
Water Supply Network ◽  
Detection Strategy ◽  
Monitoring Area

Abstract The water supply network (WSN) is subjected to leaks that compromise its service to the communities, which, however, is challenging to identify with conventional approaches before the consequences surface. This study developed Machine Learning (ML) models to detect leaks in the WDN. Water pressure data under leaking versus non-leaking conditions were generated with holistic WSN simulation code EPANET considering factors such as the fluctuating user demands, data noise, and the extent of leaks, etc. The results indicate that Artificial Neural Network (ANN), a supervised ML model, can accurately classify leaking versus non-leaking conditions; it, however, requires balanced dataset under both leaking and non-leaking conditions, which is difficult for a real WSN that mostly operate under normal service condition. Autoencoder neural network (AE), an unsupervised ML model, is further developed to detect leak with unbalanced data. The results show AE ML model achieved high accuracy when leaks occur in pipes inside the sensor monitoring area, while the accuracy is compromised otherwise. This observation will provide guidelines to deploy monitoring sensors to cover the desired monitoring area. A novel strategy is proposed based on multiple independent detection attempts to further increase the reliability of leak detection by the AE and is found to significantly reduce the probability of false alarm. The trained AE model and leak detection strategy is further tested on a testbed WSN and achieved promising results. The ML model and leak detection strategy can be readily deployed for in-service WSNs using data obtained with internet-of-things (IoTs) technologies such as smart meters.

Organics and its Control in Reclaimed Water

2014 ◽  
Vol 507 ◽  
pp. 752-756
Author(s):  
Chao Xiang ◽  
Ping Xu ◽  
Jing Wang ◽  
Tao Wang ◽  
Ya Jun Zhang
Keyword(s):  
Water Supply ◽  
Indirect Effects ◽  
Microbial Growth ◽  
Treatment Process ◽  
Reclaimed Water ◽  
Limiting Factors ◽  
Supply Network ◽  
Water Supply Network ◽  
Factors Affecting ◽  
Comprehensive Study

Some forms of organic matter existing in the water have direct or indirect effects on microbial growth. By the investigation data over drinking water and reclaimed water, we summarized organic limiting factors that may affect the growth of microorganisms and factors affecting these water qualities in the reclaimed water supply network, such as a variety of treatment process and the residual disinfectants. Through its comprehensive study, we want to make a contribution of opinion to control the growth of microorganisms in reclaimed water supply network.

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