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Parametric Nonlinear Regression Models for Dike Monitoring Systems

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Advances in Intelligent Data Analysis XIII (IDA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8819))

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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.

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© 2014 Springer International Publishing Switzerland

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de Vries, H., Azzopardi, G., Koelewijn, A., Knobbe, A. (2014). Parametric Nonlinear Regression Models for Dike Monitoring Systems. In: Blockeel, H., van Leeuwen, M., Vinciotti, V. (eds) Advances in Intelligent Data Analysis XIII. IDA 2014. Lecture Notes in Computer Science, vol 8819. Springer, Cham. https://doi.org/10.1007/978-3-319-12571-8_30

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  • DOI: https://doi.org/10.1007/978-3-319-12571-8_30

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12570-1

  • Online ISBN: 978-3-319-12571-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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