Neural Networks Approach for Predicting Slope Failure Areas Based on the Onsite Slope Survey Parameters at a Specified Mountain Road

Research Area: Volume 7,Issue 3, May 2018 Year: 2018
Type of Publication: Article Keywords: Neural Network Model, Evaluation Index, Slope Parameter, Rainfall Factor
Authors:
  • Tienfuan Kerh
  • Chih-Hung Tsenz
Journal: IJEIR Volume: 7
Number: 3 Pages: 192-199
Month: May
ISSN: 2277-5668
Abstract:
This study proposed a new method of using a neural network model for predicting slope failure areas by training different slope parameters obtained from 49 onsite slope failure surveys in a specified mountain road section. The developed model has three input parameters including slope height, slope horizontal distance, and rainfall factor. With three neurons in the hidden layer, a relatively better performance for predicting a slope failure area can be obtained in the output layer, based on the evaluation indices of a correlation coefficient and a mean square error. For all the data sets in the neural network calculation process, the square value of a correlation coefficient between the prediction result and the survey data reached up to 0.8020, which may imply that the developed model has an acceptable reliability. The current study has presented a simple way for dealing with this type of slope failure problem, and the obtained results may provide useful information for the relevant engineering agency to improve road safety in the mountain road region investigated

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