Detection of two simultaneous leakages in water distribution network using hybrid feedforward artificial neural networks

Document Type : Research Article

Authors

1 Associate Professor, Department of Civil Engineering, Faculty of Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran

2 Ph.D. Student, Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran

3 Assistant Professor, Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran

4 Assistant Professor, Department of Civil Engineering, Faculty of Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran

Abstract

The leakage is one of the main challenges in the operation of water distribution networks. In the present study, leakage location is detected using Feedforward Artificial Neural Networks (ANNs). For this purpose, two scenarios are considered for training the ANNs. In the first scenario, two simultaneous leakages with equal values, and in the second scenario, two simultaneous leakages but with unequal values are applied to every pair of network node. The training data are analyzed by EPANET2.0 hydraulic simulation software linked with the MATLAB programming language. In both scenarios, first, ANNs are trained using flow rates of total pipes number. Then, sensitivity analysis is performed by Hybrid ANNs for the flow rates of different percent of pipes number. The results of the proposed Hybrid ANNs indicate that in the first scenario, by having the flow rates of 10% of the total pipes, the locations of two simultaneous leakages are successfully determined. However, for the second scenario, while the difference between the two leakages is less than 80% of the maximum leakage (up to ratio value of 10 and 90 % leakages), by having the flow rates of 10% of the total pipes, the locations of the two leakages are still successfully determined. But for larger differences, only the location of the bigger leakage can be detected. Despite the complexities of the second scenario, the proposed ANNs could successfully detect the location of the bigger leakage.

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