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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/nhess-2017-253
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2017-253
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Jul 2017

13 Jul 2017

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This preprint has been withdrawn by the authors.

Prediction of rainfall induced landslide movements by artificial neural networks

Janko Logar1, Goran Turk1, Peter Marsden2, and Tomaž Ambrožič1 Janko Logar et al.
  • 1Faculty of Civil and Geodetic Engineering, University of Ljubljana, Jamova cesta 2, 1000 Ljubljana, Slovenia
  • 2Coastal Management Isle of Wight Council, Salisbury Gardens, Dudley Road, Ventnor, Isle of Wight, PO38 1EJ, UK

Abstract. Many slow to moderate landslides are monitored in order to react on time and prevent loss of lives and reduce material damage. In most of such cases there are very limited data on the geometry, hydrogeological and material properties of the landslide. The aim of the paper is to test the ability of artificial neural networks (ANN) to make reliable short term predictions of rainfall induced landslide movements based on normally available data: rainfall and measured displacements. The back propagation artificial neural network was trained and tested for two sliding phenomena, which are very different in nature. One is moderately moving earthflow and the other very slow landslide, with maximum rate of movements 600 mm/day and 0.094 mm/day, respectively. The results show that in both cases a trained ANN can predict landslide movements with sufficient reliability and can therefore be used together with weather forecast to assist authorities when faced with difficult decisions, such as evacuation. The accuracy of the ANN prediction of movements depends on the type and architecture of ANN as well as on the organisation of the input data used for training, as it is shown by case histories.

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Janko Logar et al.

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Janko Logar et al.

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Short summary
Paper shows that artificial intelligence (AI) is able to make reliable short term predictions of rainfall induced landslide movements based on past measurements of daily rainfall and landslide movements. The procedure has been successfully tested on two different sliding phenomena with maximum rate of movements 600 and 0.094 mm/day, respectively. The goal of the research is to use AI to support hard decisions of civil protection (e.g. evacuation) when weather forecast predicts heavy rainfall.
Paper shows that artificial intelligence (AI) is able to make reliable short term predictions of...
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