Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks
This paper presents an application for the monitoring of leaks in flood embankments by reconstructing images in electrical tomography using logistic regression machine learning methods with elastic net regularisation, PCA and wave preprocessing. The main advantage of this solution is to obtain a mor...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/23/8081 |
| _version_ | 1797507865627328512 |
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| author | Tomasz Rymarczyk Krzysztof Król Edward Kozłowski Tomasz Wołowiec Marta Cholewa-Wiktor Piotr Bednarczuk |
| author_facet | Tomasz Rymarczyk Krzysztof Król Edward Kozłowski Tomasz Wołowiec Marta Cholewa-Wiktor Piotr Bednarczuk |
| author_sort | Tomasz Rymarczyk |
| collection | DOAJ |
| description | This paper presents an application for the monitoring of leaks in flood embankments by reconstructing images in electrical tomography using logistic regression machine learning methods with elastic net regularisation, PCA and wave preprocessing. The main advantage of this solution is to obtain a more accurate spatial conductivity distribution inside the studied object. The described method assumes a learning system consisting of multiple equations working in parallel, where each equation creates a single point in the output image. This enables the efficient reconstruction of spatial images. The research focused on preparing, developing, and comparing algorithms and models for data analysis and reconstruction using a proprietary electrical tomography solution. A reliable measurement solution with sensors and machine learning methods makes it possible to analyse damage and leaks, leading to effective information and the eventual prevention of risks. The applied methods enable the improved resolution of the reconstructed images and the possibility to obtain them in real-time, which is their distinguishing feature compared to other methods. The use of electrical tomography in combination with specific methods for image reconstruction allows for an accurate spatial assessment of leaks and damage to dikes. |
| first_indexed | 2024-03-10T04:54:31Z |
| format | Article |
| id | doaj.art-a0173614fb754b7d9488856860763352 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T04:54:31Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-a0173614fb754b7d94888568607633522023-11-23T02:22:20ZengMDPI AGEnergies1996-10732021-12-011423808110.3390/en14238081Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments LeaksTomasz Rymarczyk0Krzysztof Król1Edward Kozłowski2Tomasz Wołowiec3Marta Cholewa-Wiktor4Piotr Bednarczuk5Research and Development Center, Netrix S.A., 20-704 Lublin, PolandResearch and Development Center, Netrix S.A., 20-704 Lublin, PolandFaculty of Management, Lublin University of Technology, 20-618 Lublin, PolandFaculty of Administration and Social Sciences, University of Economics and Innovation, 20-209 Lublin, PolandFaculty of Management, Lublin University of Technology, 20-618 Lublin, PolandFaculty of Transport and Computer Science, University of Economics and Innovation, 20-209 Lublin, PolandThis paper presents an application for the monitoring of leaks in flood embankments by reconstructing images in electrical tomography using logistic regression machine learning methods with elastic net regularisation, PCA and wave preprocessing. The main advantage of this solution is to obtain a more accurate spatial conductivity distribution inside the studied object. The described method assumes a learning system consisting of multiple equations working in parallel, where each equation creates a single point in the output image. This enables the efficient reconstruction of spatial images. The research focused on preparing, developing, and comparing algorithms and models for data analysis and reconstruction using a proprietary electrical tomography solution. A reliable measurement solution with sensors and machine learning methods makes it possible to analyse damage and leaks, leading to effective information and the eventual prevention of risks. The applied methods enable the improved resolution of the reconstructed images and the possibility to obtain them in real-time, which is their distinguishing feature compared to other methods. The use of electrical tomography in combination with specific methods for image reconstruction allows for an accurate spatial assessment of leaks and damage to dikes.https://www.mdpi.com/1996-1073/14/23/8081electrical tomographysensorsmachine learningPCAelastic netwave preprocessing |
| spellingShingle | Tomasz Rymarczyk Krzysztof Król Edward Kozłowski Tomasz Wołowiec Marta Cholewa-Wiktor Piotr Bednarczuk Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks Energies electrical tomography sensors machine learning PCA elastic net wave preprocessing |
| title | Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks |
| title_full | Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks |
| title_fullStr | Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks |
| title_full_unstemmed | Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks |
| title_short | Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks |
| title_sort | application of electrical tomography imaging using machine learning methods for the monitoring of flood embankments leaks |
| topic | electrical tomography sensors machine learning PCA elastic net wave preprocessing |
| url | https://www.mdpi.com/1996-1073/14/23/8081 |
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