Leak detection in water distribution network using machine learning techniques

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ABSTRACT

Leakage in the water distribution system (WDS) and its control has been challenging for water resources fraternity for management of precious water demand. This study examines an inverse engineering technique to find the leaks in water supply pipelines. The main objective of the study has been to identify the patterns of deviations in the pressure/flow in the network, due to a single leak in the network, by solving classification and regression problems using artificial neural networks (ANNs) and support vector machines (SVMs). The leak detections were solved using two scenarios, wherein, (a) only pressure measurements and (b) only flow measurements, are undertaken in the system. The multi-layered perceptron (MLP) model and multi-label multi-class SVM classification and regression models were developed and trained using the pressure and flow signals, separately. It was found that the ANN model performed better than the SVM model in pressure- and flow-based leak detection in both classification and regression problems. The model performance could also be improved by optimizing the number of inputs to the model during the training phase. The present study would be useful for water supply management while applying the techniques for minimizing the losses in the water supply network due to leakages.

KEYWORDS:

• leak detection
• artificial neural network
• support vector machines
• EPANET software
• MATLAB programming

Acknowledgements

The authors would like to acknowledge the Centre of Excellence on “Water Resources and Flood Management” at Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology – Surat, Gujarat, India established under TEQIP-II grant of Ministry of Education for providing the required facilities and infrastructural support. Authors are thankful to the Editor, Associate Editor and Reviewers for their comments which helped in improvement of readability of the present paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The distribution network data used and results from regression analysis in this study are available in the Appendix A & B after the references. Any other data related to study will be available based on the request for academic purposes only. Interested readers may directly contact the corresponding author for any other data requirements.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.