Parametric Nonlinear Regression Models for Dike Monitoring Systems

Parametric Nonlinear Regression Models for Dike Monitoring Systems

de Vries, H.W.W.
Azzopardi, G.
Koelewijn, A.
Knobbe, A.

Blockeel, H. (editor)
van Leeuwen, M. (editor)
Vinciotti, V. (editor)

2014

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.

Communication & Information
BIS - Business Information Services
TS - Technical Sciences
Infostructures
Informatics
Information Society
Anomaly detection
Dike monitoring
Nonlinear regression
Structural health monitoring
Levees
Mathematical models
Pore pressure
Pressure sensors
Regression analysis
Sensor networks
Water levels
Domain knowledge
Environmental factors
Goodness of fit
Monitoring system
Hydraulic structures

http://resolver.tudelft.nl/uuid:c64b9603-3540-4c35-a70b-9c4258e58076

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

520199

Springer Verlag, Switzerland

Advances in Intelligent Data Analysis XIII - Proceedings 13th International Symposium, IDA 2014, 30 October – 1 November 2014, Leuven, Belgium,, 345-355

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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