Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/26327
Title: Parametric nonlinear regression models for dike monitoring systems
Other Titles: Advances in intelligent data analysis XIII. IDA 2014. Lecture notes in computer science
Authors: Vries, Harm de
Azzopardi, George
Koelewijn, Andre
Knobbe, Arno
Keywords: Structural health monitoring
Dikes (Engineering)
Anomaly detection (Computer security)
Issue Date: 2014
Publisher: Springer, Cham
Citation: De Vries, H., Azzopardi, G., Koelewijn, A., & Knobbe, A. (2014). Parametric nonlinear regression models for dike monitoring systems. In H. Blockeel., M. Van Leeuwen., & V. Vinciotti. (Eds.), Advances in intelligent data analysis XIII. IDA 2014. Lecture notes in computer science, vol 8819. (pp. 345-355). Springer, Cham.
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.
Description: A conference paper presented by the authors at the 13th International Symposium, IDA 2014, Advances in Intelligent Data Analysis XIII., Leuven, Belgium, October 30 – November 1, 2014
URI: https://www.um.edu.mt/library/oar//handle/123456789/26327
ISBN: 9783319125701
Appears in Collections:Scholarly Works - FacICTAI

Files in This Item:
File Description SizeFormat 
BookChapter_Parametric Nonlinear Regression Models.pdf523.3 kBAdobe PDFView/Open


Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.