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It is estimated that about 20% of treated drinking water is lost through distribution pipeline leakages in the United States. Pipeline leakage detection is a top priority for water utilities across the globe as leaks increase operational energy consumption and could also develop into potentially catastrophic water main breaks, if left unaddressed. Leakage detection is a laborious task often limited by the financial and human resources that utilities can afford. Many conventional leak detection techniques also only offer a snapshot indication of leakage presence. Furthermore, the reliability of many leakage detection techniques on plastic pipelines that are increasingly preferred for drinking water applications is questionable. As part of a smart water utility framework, this paper proposes and validates a hydraulic model-based technique for detecting and assessing the severity of leakages in buried water pipelines through monitoring of pressure from across the water distribution system (WDS). The envisioned smart water utility framework entails the capabilities to collect water consumption data from a limited number of WDS nodes and pressure data from a limited number of pressure monitoring stations placed across the WDS. A popular benchmark WDS is initially modified by inducing leakages through addition of orifice nodes. The leakage severity is controlled using emitter coefficients of the orifice nodes. WDS pressure data for various sets of demands is subsequently gathered from locations where pressure monitoring stations are to be placed in that modified distribution network. An evolutionary optimization algorithm is subsequently used to predict the emitter coefficients so as to determine the leakage severities based on the hydraulic dependency of the monitored pressure data on various sets of nodal demands. Artificial neural networks (ANNs) are employed to mimic the popular hydraulic solver EPANET 2.2 for high computational efficiency. The goals of this study are to: (1) validate the proof of concept of the proposed modeling approach for detecting and assessing the severity of leakages and (2) evaluate the sensitivity of the prediction accuracy to number of pressure monitoring stations and number of demand nodes at which consumption data is gathered and used. This study offers new value to prioritize pipes for rehabilitation by predicting leakages through a hydraulic model-based approach.
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Water Leakage Detection Using Neural Networks
The primary goal of the project is to leverage recent developments in smart water technologies to detect and reduce water leakages in large water distribution networks with the aid of neural networks. A cost effective non-invasive solution to detect leakages in transmission pipelines is needed by many water utilities as it will lead to significant water savings and reduced pipe breakage frequencies, especially in older infrastructure systems. The eventual goal of the project is to test the ANN model on a real network using field measured pressure and pipe breakage data after tuning and developing the model with simulated data. In this project we propose building a regression model, based on Multi-Layer Perceptron (MLP) algorithm, which is a class of feedforward Artificial Neural Networks (ANNs) to detect the leak locations within a proposed network. The model should be able to learn the structure, i.e. mapping of various leak nodes and sensor nodes in an area, such that it can detect the leak nodes based on the pressure values with significant accuracy.
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- Award ID(s):
- 1919228
- PAR ID:
- 10282254
- Date Published:
- Journal Name:
- World Environmental and Water Resources Congress 2021
- Page Range / eLocation ID:
- 1033 to 1040
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1061/9780784483466.096
