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International Journal of Geophysics
Volume 2018, Article ID 4861254, 9 pages
Research Article

Machine Learning: A Novel Approach to Predicting Slope Instabilities

Mining & Geological Engineering, University of Arizona, Tucson, AZ, USA

Correspondence should be addressed to Upasna Chandarana Kothari; moc.liamg@pansapu

Received 14 August 2017; Revised 16 January 2018; Accepted 18 January 2018; Published 20 February 2018

Academic Editor: Yun-tai Chen

Copyright © 2018 Upasna Chandarana Kothari and Moe Momayez. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Geomechanical analysis plays a major role in providing a safe working environment in an active mine. Geomechanical analysis includes but is not limited to providing active monitoring of pit walls and predicting slope failures. During the analysis of a slope failure, it is essential to provide a safe prediction, that is, a predicted time of failure prior to the actual failure. Modern-day monitoring technology is a powerful tool used to obtain the time and deformation data used to predict the time of slope failure. This research aims to demonstrate the use of machine learning (ML) to predict the time of slope failures. Twenty-two datasets of past failures collected from radar monitoring systems were utilized in this study. A two-layer feed-forward prediction network was used to make multistep predictions into the future. The results show an 86% improvement in the predicted values compared to the inverse velocity (IV) method. Eighty-two percent of the failure predictions made using ML method fell in the safe zone. While 18% of the predictions were in the unsafe zone, all the unsafe predictions were within five minutes of the actual failure time, all practical purposes making the entire set of predictions safe and reliable.