Leakage detection in water distribution networks using hybrid feedforward artificial neural networks
Water leakage control in water distribution networks (WDNs) is one of the main challenges of water utilities. The present study proposes a new method to locate a leakage in WDNs using feedforward artificial neural networks (ANNs). For this purpose, two ANNs training cases are considered. For case 1,...
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| Main Authors: | , , , |
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| Format: | article |
| Language: | EN |
| Published: |
IWA Publishing
2021
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| Subjects: | |
| Online Access: | https://doaj.org/article/daa4e5d4b2c045fc85062b5d2fb0e8b7 |
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| Summary: | Water leakage control in water distribution networks (WDNs) is one of the main challenges of water utilities. The present study proposes a new method to locate a leakage in WDNs using feedforward artificial neural networks (ANNs). For this purpose, two ANNs training cases are considered. For case 1, the ANNs are trained by average daily water demand, including small to large hypothetical leakages. In case 2, the ANNs are trained by hourly water demand and variable hourly nodal leakages over 24 hours. The training parameters are determined by EPANET2.0 hydraulic simulation software using MATLAB programming language. In both cases, first, ANNs are trained using flow rates of total pipes number. Then, sensitivity analysis is performed by hybrid ANNs for the flow rates of pipes number less than the number of the total pipes. The results of proposed hybrid ANNs indicate that if at least the flow rates of 10% of the total pipes are known (using flowmeters), then the leakage locations in both cases can be determined. Despite the complexity of case 2, because of the variations of demand and leakage over the 24-hour period, the proposed method could detect the leakage location with high accuracy. HIGHLIGHTS
A leakage detection algorithm for WDNs using feedforward ANNs.;
ANNs were applied for two cases: average daily water demand and hypothetical leakages; variable water demand and nodal leakages over the 24-hour.;
ANNs training based on pipe flow rates and nodal leakage.;
A sensitivity analysis using Hybrid ANNs.;
The proposed algorithm successfully detects the leakage locations in both cases by the flow rates of 10% pipes.; |
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