Preprints
https://doi.org/10.5194/nhess-2017-176
https://doi.org/10.5194/nhess-2017-176

  29 May 2017

29 May 2017

Status: this preprint has been retracted.

Assessment of shallow landslide susceptibility using an artificial neural network in Enshi region, China

Bin Zeng1, Wei Xiang2, Joachim Rohn3, Dominik Ehret4, and Xiaoxi Chen1 Bin Zeng et al.
  • 1School of Environmental Studies, China University of Geosciences, Wuhan 430074, Hubei, China
  • 2Faculty of Engineering, China University of Geosciences, Wuhan 430074, Hubei, China
  • 3GeoZentrum Nordbayern, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen 91054, Germany
  • 4Dept. 95 – State Engineering Geology, State Office for Geology, Resources, and Mining, 79104 Freiburg i. Br., Germany

Abstract. Landslides are one of the most common and damaging natural hazards in mountainous areas. However, due to the complex mechanisms that influence the activation of landslides, it can often be very difficult to predict exactly when a landslide will occur. Therefore, research on landslide prevention and mitigation mainly focuses on the distribution forecasting of unstable slopes that are prone to landslides in specific regions and under multiple external forces. The prediction of the spatial distribution of these unstable slopes, termed Landslide Susceptibility Zonation, is important in helping with government land-use planning and in reducing unnecessary loss of life and property. Researching unstable slopes in the Silurian stratum in Enshi region, China, this investigation established a GIS and artificial neural network (ANN)-based method to predict the distribution of potential landslides in this area. Based on the failure mechanism analysis of typical landslides in Silurian stratum, development of evaluation index system which represents the most relevant factors that influence the slope stability, and establishment of intelligent slope stability susceptibility prediction model by artificial neural network, the spatial distribution of unstable slope zones that are prone to landslides were predicted in the study area. The results were further well supported from remote sensing data and field investigations. This research proves that the spatial unstable slope prediction method based on intelligence theory and GIS technology is accurate and reliable.

This preprint has been retracted.

Bin Zeng et al.

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Bin Zeng et al.

Bin Zeng et al.

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This preprint has been retracted.

Short summary
Landslides are one of the most common and damaging natural hazards in mountainous areas. Based on the failure mechanism analysis, a targeted evaluation factor index system was developed, and a prediction model combined GIS technology with artificial neural network was established for predicting distribution of unstable slope zones that are prone to landslides in the study area. The result proves that the prediction method based on intelligence theory and GIS technology is accurate and reliable.
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