Open AccessPredicting subsurface soil layering and landslide risk with Artificial Neural Networks: a case study from Iran
Author(s) -
F. Farrokhzad,
Amin Barari,
Lars Bo Ibsen,
Asskar Janalizadeh Choobbasti
Publication year - 2011
Publication title -
geologica carpathica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.702
H-Index - 40
eISSN - 1336-8052
pISSN - 1335-0552
DOI - 10.2478/v10096-011-0034-7
Subject(s) - layering , landslide , artificial neural network , geology , borehole , sampling (signal processing) , geotechnical engineering , soil science , hydrology (agriculture) , mining engineering , machine learning , engineering , computer science , botany , biology , filter (signal processing) , electrical engineering
Predicting subsurface soil layering and landslide risk with Artificial Neural Networks: a case study from Iran This paper is concerned principally with the application of Artificial Neural Networks (ANN) in geotechnical engineering. In particular the application of ANN is discussed in more detail for subsurface soil layering and landslide analysis. Two ANN models are trained to predict subsurface soil layering and landslide risk using data collected from a study area in northern Iran. Given the three-dimensional coordinates of soil layers present in thirty boreholes as training data, our first ANN successfully predicted the depth and type of subsurface soil layers at new locations in the region. The agreement between the ANN outputs and actual data is over 90 % for all test cases. The second ANN was designed to recognize the probability of landslide occurrence at 200 sampling points which were not used in training. The neural network outputs are very close (over 92 %) to risk values calculated by the finite element method or by Bishop's method.