State Estimation of Municipal Water Supply Network Based on BP Neural Network and Genetic Algorithm

In order to realize real-time leakage (online) diagnosis, through correlation analysis of the network leakage and pressure variation, using the established microscopic model as the basic hydraulic analysis model, using the position of the network leakage point and the water of leakage point as the variables, using minimizing difference of the network pressure points monitoring value and simulation value as the target when leakage occurs, establish inverse transient leakage location of water supply network model based on genetic algorithm. Finally through the two modes of leakage verification, the result shows that this model can effectively achieve the network leakage location and quantitative.

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Hybrid Multi-Objective Genetic Algorithm for Optimal Design of Water Supply Network

Water Distribution Systems Analysis 2010 ◽

10.1061/41203(425)82 ◽

2011 ◽

Author(s):

Haixing Liu ◽

Yixing Yuan ◽

Ming Zhao ◽

Haien Fang ◽

Hongbin Zhao

Keyword(s):

Genetic Algorithm ◽

Optimal Design ◽

Water Supply ◽

Supply Network ◽

Water Supply Network ◽

Multi Objective ◽

Multi Objective Genetic Algorithm

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

Journal of Infrastructure Preservation and Resilience ◽

10.1186/s43065-021-00021-6 ◽

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.

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