Leak detection using random forest and pressure simulation

Aymon, Lucien (School of Engineering, HES-SO Valais-Wallis, HEI, HES-SO // University of Applied Sciences Western Switzerland) ; Decaix, Jean (School of Engineering, HES-SO Valais-Wallis, HEI, HES-SO // University of Applied Sciences Western Switzerland) ; Carrino, Francesco (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Mudry, Pierre-André (School of Engineering, HES-SO Valais-Wallis, HEI, HES-SO // University of Applied Sciences Western Switzerland) ; Mugellini, Elena (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Abou Khaled, Omar (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Baltensperger, Richard (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland)

Water is a scarce resource which is becoming increasingly inaccessible. It is therefore necessary, in most parts of the world, to capture, transport and allocate it efficiently and thoughtfully. The implementation of monitored water distribution networks is often expensive. The purpose of this project is therefore to monitor leakage and consumption in a non-pressurized agricultural irrigation system using only inexpensive and easily installed pressure sensors. We modeled the water network to automatically simulate a leak randomly through the network. These simulated pressures serve as a dataset to train, test and validate a Random Forest algorithm that detects the leaks. Through pressure measures, the model can locate the junction closest to the leak with an accuracy of 96.24%. This approach therefore allows leaks detection in a water distribution system without the use of expensive flow sensors.


Keywords:
Conference Type:
short paper
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
HEI-VS
Institute:
HumanTech - Technology for Human Wellbeing Institute
Institut Systèmes industriels
Publisher:
Bern, Switzerland, 14 June 2019
Date:
2019-06
Bern, Switzerland
14 June 2019
Pagination:
2 p.
Published in:
Proceedings of 2019 6th Swiss Conference on Data Science (SDS), 14 June 2019, Bern, Switzerland
DOI:
ISBN:
978-1-7281-3105-4
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2020-01-21, last modified 2020-01-21

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