Experimental Analysis of Various Deep Learning Methods for Predicting Displacements in an Open Pit Coal Mine

Abstract

Slope failure is a problem that can occur on slopes formed by natural or unnatural means for various reasons and as a result, can cause serious loss of life and property. This is also crucial in open pit mines. Therefore, it is critical to constantly monitor slope deformations and predict a possible slide that may occur in the future. Predicting the initial trend behavior of a created slope is at least as important as estimating slope failure. Therefore, in this study, we tried to estimate slope stability with a small number of deformation data. In this study we aimed to compare several deep learning methods in using time series prediction with limited data. For this purpose, we applied MLP, GRU, LSTM Networks, biLSTM and a hybrid structure of CNN-RNN methods. We utilized data taken from three stations settled in an open pit mine slope and predicted the compound deformation value of x-, y- and z-direction in these slopes. The performance was compared with respect to the root mean squared error (RMSE) and coefficient of correction (R2) values. The minimum RMSE was obtained as 0.2293 and maximum R2 was reached as 0.9984 by the GRU method on the third station's data.