Parametric Nonlinear Regression Models for Dike Monitoring Systems
bookPart
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.
Topics
TNO Identifier
520199
Publisher
Springer Verlag
Source title
Advances in Intelligent Data Analysis XIII - Proceedings 13th International Symposium, IDA 2014, 30 October – 1 November 2014, Leuven, Belgium,
Editor(s)
Blockeel, H.
Leeuwen, M. van
Vinciotti, V.
Leeuwen, M. van
Vinciotti, V.
Place of publication
Switzerland
Pages
345-355
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