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Prediction of Displacements in Unstable Areas Using a Neural Model

Author

Listed:
  • E. Binaghi
  • M. Boschetti
  • P.A. Brivio
  • I. Gallo
  • F. Pergalani
  • A. Rampini

Abstract

In pipeline management the accurate prediction of weak displacements is a crucial factor in drawing up a prevention policy since the accumulation of these displacements over a period of several years can lead to situations of high risk. This work addresses the specific problem related to the prediction of displacements induced by rainfall in unstable areas, of known geology, and crossed by underground pipelines. A neural model has been configured which learns of displacements from instrumented sites (where inclinometric measurements are available) and is able to generalise to other sites not equipped with inclinometers. Copyright Kluwer Academic Publishers 2004

Suggested Citation

  • E. Binaghi & M. Boschetti & P.A. Brivio & I. Gallo & F. Pergalani & A. Rampini, 2004. "Prediction of Displacements in Unstable Areas Using a Neural Model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 32(1), pages 135-154, May.
    • Handle: RePEc:spr:nathaz:v:32:y:2004:i:1:p:135-154
    DOI: 10.1023/B:NHAZ.0000026796.59079.1a
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    References listed on IDEAS

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    1. E. Binaghi & L. Luzi & P. Madella & F. Pergalani & A. Rampini, 1998. "Slope Instability Zonation: a Comparison Between Certainty Factor and Fuzzy Dempster–Shafer Approaches," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 17(1), pages 77-97, January.
    2. L. Luzi & F. Pergalani, 1999. "Slope Instability in Static and Dynamic Conditions for Urban Planning: the ‘Oltre Po Pavese’ Case History (Regione Lombardia – Italy)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 20(1), pages 57-82, July.
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    Cited by:

    1. Alessandro Pagano & Raffaele Giordano & Ivan Portoghese & Umberto Fratino & Michele Vurro, 2014. "A Bayesian vulnerability assessment tool for drinking water mains under extreme events," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(3), pages 2193-2227, December.
    2. Chong Xu & Xiwei Xu & Fuchu Dai & Zhide Wu & Honglin He & Feng Shi & Xiyan Wu & Suning Xu, 2013. "Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(2), pages 883-900, September.

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