Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
A Comparison Between Machine Learning Techniques to Find Leaks in Pipe Networks
Dissertation (MEng)--University of Pretoria, 2017.
Saved in:
| Other Authors: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
University of Pretoria
2018
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613498420232192 |
|---|---|
| access_status_str | Open Access |
| author2 | Heyns, P.S. (Philippus Stephanus) |
| author_browse | Heyns, P.S. (Philippus Stephanus) |
| author_facet | Heyns, P.S. (Philippus Stephanus) |
| collection | Thesis |
| dc_rights_str_mv | © 2018 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng)--University of Pretoria, 2017. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/66386 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:06.348Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/66386 A Comparison Between Machine Learning Techniques to Find Leaks in Pipe Networks Heyns, P.S. (Philippus Stephanus) Wilke, Daniel Nicolas Van der Walt, Joseph Cornelius UCTD Machine learning Pipe networks Leak detection Engineering, built environment and information technology theses SDG-06 SDG-06: Clean water and sanitation Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-11 SDG-11: Sustainable cities and communities Dissertation (MEng)--University of Pretoria, 2017. In 2012, the National Non-Revenue Water assessment revealed that South Africa has 37% of non-revenue water. With the steadily growing demand for this scarce resource, the detection of leaks in pipe networks is becoming more important. Currently, in South Africa the primary method of detecting leaks is to install pressure management systems and monitoring minimum night time ows [1]. The pressure- ow deviation method, can be used to formulate an inverse analysis model based leak detection problem. This problem can then be solved using Arti cial Neural Networks, Support Vector Machines and other optimization methods. With EPANET, di erent networks were tested to compare these methods to nding leaks, using an inverse analysis formulated problem. Four di erent numerical networks were modeled and tested, a simple single pipe network, a small agricultural site, a distribution network proposed and investigated by Poulakis et al. [2] and the simulated model of the experimental network that was designed and commissioned during the study in our laboratory. From the numerical investigation, it was found that the optimization methods struggled to nd solutions for simple networks with in nite number of solutions for the problem. For more complex numerical networks, it was seen that the Support Vector machine and the Arti cial Neural Networks trained to the averages of their respective data sets. Errors to ensure an accurate solution found by these algorithms were calculated as 2:6% for the numerical experimental network. The experimental network consisted of six possible leaking pipes, each having a length of 3m and a diameter of 10mm. Three leak cases were tested with diameters of 3mm and 2mm. Overall, the Support Vector machine could locate the leaking pipe with the best accuracy, while the minimizing of non-regularized error could calculate the size and location of the leak the most accurately. Multiple leak cases were measured with the experimental network. The Support Vector machine was tested on these measurements, where it was found that two of the three leak cases could be solved with relative accuracies. Sensor usage optimization was completed on the measurements for the experimental network, where it was found that the leaks could be classi ed correctly with probabilities higher than 98% if only two sensors were used in the training of the SVM instead of all twelve. Overall this method of leak detection shows promise for certain applications in the future. With practical applications on water distribution, transportation, and agricultural networks. mi2025 Mechanical and Aeronautical Engineering MEng Unrestricted SDG-06: Clean water and sanitation SDG-09: Industry, innovation and infrastructure SDG-11: Sustainable cities and communities 2018-08-30T09:03:26Z 2018-08-30T09:03:26Z 2018 2017 Dissertation Van der Walt, JC 2017, A Comparison Between Machine Learning Techniques to Find Leaks in Pipe Networks, MEng Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/66386> A2018 http://hdl.handle.net/2263/66386 en © 2018 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Machine learning Pipe networks Leak detection Engineering, built environment and information technology theses SDG-06 SDG-06: Clean water and sanitation Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-11 SDG-11: Sustainable cities and communities A Comparison Between Machine Learning Techniques to Find Leaks in Pipe Networks |
| title | A Comparison Between Machine Learning Techniques to Find Leaks in Pipe Networks |
| title_full | A Comparison Between Machine Learning Techniques to Find Leaks in Pipe Networks |
| title_fullStr | A Comparison Between Machine Learning Techniques to Find Leaks in Pipe Networks |
| title_full_unstemmed | A Comparison Between Machine Learning Techniques to Find Leaks in Pipe Networks |
| title_short | A Comparison Between Machine Learning Techniques to Find Leaks in Pipe Networks |
| title_sort | comparison between machine learning techniques to find leaks in pipe networks |
| topic | UCTD Machine learning Pipe networks Leak detection Engineering, built environment and information technology theses SDG-06 SDG-06: Clean water and sanitation Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-11 SDG-11: Sustainable cities and communities |
| url | http://hdl.handle.net/2263/66386 |