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Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks

Author

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  • Tomasz Rymarczyk

    (Research and Development Center, Netrix S.A., 20-704 Lublin, Poland
    Faculty of Transport and Computer Science, University of Economics and Innovation, 20-209 Lublin, Poland)

  • Krzysztof Król

    (Research and Development Center, Netrix S.A., 20-704 Lublin, Poland
    Faculty of Transport and Computer Science, University of Economics and Innovation, 20-209 Lublin, Poland)

  • Edward Kozłowski

    (Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland)

  • Tomasz Wołowiec

    (Faculty of Administration and Social Sciences, University of Economics and Innovation, 20-209 Lublin, Poland)

  • Marta Cholewa-Wiktor

    (Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland)

  • Piotr Bednarczuk

    (Faculty of Transport and Computer Science, University of Economics and Innovation, 20-209 Lublin, Poland)

Abstract

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.

Suggested Citation

  • Tomasz Rymarczyk & Krzysztof Król & Edward Kozłowski & Tomasz Wołowiec & Marta Cholewa-Wiktor & Piotr Bednarczuk, 2021. "Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks," Energies, MDPI, vol. 14(23), pages 1-35, December.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8081-:d:693884
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    References listed on IDEAS

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    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Mevik, Björn-Helge & Wehrens, Ron, 2007. "The pls Package: Principal Component and Partial Least Squares Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 18(i02).
    3. Tomasz Rymarczyk & Grzegorz Kłosowski & Anna Hoła & Jan Sikora & Tomasz Wołowiec & Paweł Tchórzewski & Stanisław Skowron, 2021. "Comparison of Machine Learning Methods in Electrical Tomography for Detecting Moisture in Building Walls," Energies, MDPI, vol. 14(10), pages 1-22, May.
    4. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    5. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    Cited by:

    1. Tao Liu & Jiayuan Yu & Yuanjin Zheng & Chao Liu & Yanxiong Yang & Yunfei Qi, 2022. "A Nonlinear Multigrid Method for the Parameter Identification Problem of Partial Differential Equations with Constraints," Mathematics, MDPI, vol. 10(16), pages 1-12, August.
    2. Olena Borysiak & Tomasz Wołowiec & Grzegorz Gliszczyński & Vasyl Brych & Oleksandr Dluhopolskyi, 2022. "Smart Transition to Climate Management of the Green Energy Transmission Chain," Sustainability, MDPI, vol. 14(18), pages 1-11, September.
    3. Bartosz Przysucha & Dariusz Wójcik & Tomasz Rymarczyk & Krzysztof Król & Edward Kozłowski & Marcin Gąsior, 2023. "Analysis of Reconstruction Energy Efficiency in EIT and ECT 3D Tomography Based on Elastic Net," Energies, MDPI, vol. 16(3), pages 1-22, February.
    4. Olena Borysiak & Łukasz Skowron & Vasyl Brych & Volodymyr Manzhula & Oleksandr Dluhopolskyi & Monika Sak-Skowron & Tomasz Wołowiec, 2022. "Towards Climate Management of District Heating Enterprises’ Innovative Resources," Energies, MDPI, vol. 15(21), pages 1-16, October.

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