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We describe the detection methods and the results of anomalous conditions in dikes (earthen dams/ levees) based on a simultaneous processing of several data streams originating from sensors installed in these dikes. Applied methods are especially valuable in cases where lack of information or computational resources prohibit computing the state of the dike with finite element and other mathematical models. The data-driven methods are part of the artificial intelligence (AI) component of the 'Urbanflood' early warning system. This AI component includes pre-processing (e.g., gap filling and measurements synchronization procedures) of data streams, feature extraction and anomaly detection by one-side (also known as one-class) classification methods. Our approach has been successfully validated during a non-destructive piping experiment at the Zeeland dike (The Netherlands).

Data-driven modelling for flood defence structure analysisPyayt, A.; Mokhov, I.I.; Kozionov, A.P.; Kusherbaeva, V.T.; Lang, B.; Krzhizhanovskaya, V.; Meijer, R.J. General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Download date: 10 May 2018 301Comprehensive Flood Risk Management -Klijn & Schweckendiek (eds) © 2013 Taylor & Francis Group, London, ISBN 978-0-415-62144-1 Data-driven modelling for flood defence structure analysis ABSTRACT: We present a data-driven modelling approach for detection of anomalies in flood defences (levees, dykes, dams, embankments) equipped with sensors. An auto-regressive linear model and feed-forward neural network were applied for modelling a transfer function between the sensors. This approach has been validated on a dike in Boston, UK-one of the pilot sites of the UrbanFlood projectthat showed both normal and abnormal sensor behaviour. Comparison of the linear and non-linear models is presented. The suggested model-based anomaly detection approach will extend functionality of the developed Artificial Intelligence component of the UrbanFlood Early Warning System.

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