Artificial intelligence and finite element modelling for monitoring flood defence structures

Pyayt, A.L.; Mokhov, I.I.; Kozionov, A.P.; Kusherbaeva, Victoria; Melnikova, N. B.; Krzhizhanovskaya, Valeria V.; Meijer, Robert

doi:10.1109/eesms.2011.6067047

Cited by 20 publications

(18 citation statements)

References 16 publications

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“…This hybrid approach combining data-driven and physics-based modeling has been tested on one of the pilot sites -Livedike, a sea dike in Groningen, the Netherlands. FEM simulations have been used for training artificial intelligence system on "normal" and "abnormal" virtual sensor dynamics; the results have been published in [30].…”

Section: Integration Of the Virtual Dike Module Into The Urbanflood Ementioning

confidence: 99%

“…A more detailed analysis of sensor data has been reported in [31], where a data-driven approach with a neural network were applied for modeling a transfer function between the sensors within the Artificial Intelligence software module.…”

Section: Instrumentation and Sensor Data Analysismentioning

confidence: 99%

“…• Development of a decision support system for public authorities and citizens that will help making informed decisions in case of emergency and in routine levee quality assessment, thus reducing flood risk and providing advanced tools for flood management. mechanisms [25][26][27], general design and implementation of an EWS [19,29], development of EWS components for simulation of dike breaching and flood spreading [15,21,22], city evacuation [28], and development of an artificial intelligence (AI) system employing data-driven methods [31,32] and its application in conjunction with the finite element analysis module [30,10,11].…”

Section: Introductionmentioning

confidence: 99%

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Experience of using FEM for real-time flood early warning systems: Monitoring and modeling Boston levee instability

Melnikova

Jordan

Krzhizhanovskaya

2015

Journal of Computational Science

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Section: Integration Of the Virtual Dike Module Into The Urbanflood Ementioning

confidence: 99%

Section: Instrumentation and Sensor Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Experience of using FEM for real-time flood early warning systems: Monitoring and modeling Boston levee instability

Melnikova

Jordan

Krzhizhanovskaya

2015

Journal of Computational Science

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“…One of the next steps is validation of the modeldriven anomaly detection approach by application of the Virtual Dike model, in a way similar to our previous work (Pyayt et al 2011b).…”

Section: Future Stepsmentioning

confidence: 97%

“…A combination of this approach with finite element Virtual Dike model was presented in (Pyayt et al 2011b). One-side classification approach based on Neural Clouds (Lang et al 2008) was used for dike behaviour assessment.…”

mentioning

confidence: 99%

Data-driven modelling for flood defence structure analysis

Pyayt¹,

Mokhov²,

Kozionov³

et al. 2012

Comprehensive Flood Risk Management

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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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