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Self-organizing-Map Analysis of InSAR Time Series for the Early Warning of Structural Safety in Urban Areas

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

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Abstract

Among the many causes of collapse of civil structures, those related to the downfall of foundations are crucial for their likely catastrophic consequences. Interferometric synthetic aperture radar (InSAR) techniques may help monitoring the time evolution of ground displacements affecting engineered structures in large urban areas. Artificial neural networks can be exploited to analyze the huge amount of data that is collected over long periods of time on very dense grid of geographical points. The paper presents a neural network-based analysis tool, able to evidence similarities among time series acquired in different points and times. This tool could support an early-warning system, aiming to forecast critical events in urban areas. The implemented procedure is tested on a dataset of InSAR time series recorded over an area of the city of London.

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Acknowledgements

Dr. Pietro Milillo of Jet Propulsion Laboratory, CALTECH (Pasadena, USA) is gratefully acknowledged for giving the database used in this paper and precious suggestions.

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Authors and Affiliations

  1. University of Cagliari, Cagliari, Italy

    Augusto Montisci & Maria Cristina Porcu

Authors
  1. Augusto Montisci
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  2. Maria Cristina Porcu
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Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Montisci, A., Porcu, M.C. (2020). Self-organizing-Map Analysis of InSAR Time Series for the Early Warning of Structural Safety in Urban Areas. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12255. Springer, Cham. https://doi.org/10.1007/978-3-030-58820-5_62

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  • DOI: https://doi.org/10.1007/978-3-030-58820-5_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58819-9

  • Online ISBN: 978-3-030-58820-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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