Slope engineering is a key project in open-pit coal mines. The stability of the slope is closely related to safety production of coal mines. Slope stability prediction is a prerequisite in slope control, faced with complexities. To quickly and effectively determine the coal mine slope stability, this paper establishes a prediction model using the random forest algorithm. Six factors influencing the slop stability were selected as input of the prediction model, including the gravity density of rocks, cohesive force, internal friction angle, slope angle, slope height and pore water pressure, and slope stability status was selected as output of the prediction model. The random forest algorithm was used to establish the nonlinear relationship between slope stability factors and stability status. The 30 sets of measured data were used as training data set to learn and train the random forest slope stability prediction model. In addition, 12 groups of data as slope stability test data were used to test the trained prediction models. In the meantime, the accuracy of the random forest prediction models was tested by comparing them with the SVM and BP neural network prediction models. The results show that the random forest prediction model based on the selected six factors has less manual control parameters, simple structure and high accuracy. The predictive results coincide with the actual state of the slope project, indicating that the prediction model is able to predict the slope stability effectively and provide guidance to coal mine slope prevention work.
WEN Tingxin
,
ZHANG Bo
. Prediction Model for Open-pit Coal Mine Slope Stability Based on Random Forest[J]. Science & Technology Review, 2014
, 32(4-5)
: 105
-109
.
DOI: 10.3981/j.issn.1000-7857.2014.h1.018
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