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Parametric Nonlinear Regression Models for Dike Monitoring Systems

  • Harm de Vries
  • George Azzopardi
  • André Koelewijn
  • Arno Knobbe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8819)

Abstract

Dike monitoring is crucial for protection against flooding disasters, an especially important topic in low countries, such as the Netherlands where many regions are below sea level. Recently, there has been growing interest in extending traditional dike monitoring by means of a sensor network. This paper presents a case study of a set of pore pressure sensors installed in a sea dike in Boston (UK), and which are continuously affected by water levels, the foremost influencing environmental factor. We estimate one-to-one relationships between a water height sensor and individual pore pressure sensors by parametric nonlinear regression models that are based on domain knowledge. We demonstrate the effectiveness of the proposed method by the high goodness of fits we obtain on real test data. Furthermore, we show how the proposed models can be used for the detection of anomalies.

Keywords

Structural health monitoring dike monitoring nonlinear regression anomaly detection 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Harm de Vries
    • 1
    • 2
    • 3
  • George Azzopardi
    • 2
    • 3
  • André Koelewijn
    • 4
  • Arno Knobbe
    • 1
  1. 1.Leiden Institute of Advanced Computer ScienceLeiden UniversityLeidenThe Netherlands
  2. 2.TNOGroningenThe Netherlands
  3. 3.Johann Bernoulli Institute for Mathematics and Computer ScienceUniversity of GroningenGroningenThe Netherlands
  4. 4.DeltaresDelftThe Netherlands

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