An improved fractal prediction model for forecasting mine slope deformation using GM (1, 1)

Abstract

The forecasting slope deformation potential is required to evaluate slope safety during open-pit mining, allowing us to formulate and promote effective emergency strategies in advance to prevent slope failure disasters. Although fractal models have been used to predict slope deformation, such limitations as low prediction accuracy, poor stability and the requirement for large amounts of data must be overcome. This article proposes an improved fractal model to forecast mine slope deformation using the grey system theory. The GM (1, 1) model is used in the improved fractal model to optimize the fitting function of the fractal dimension because of its high computational efficiency and strong fitting ability. Data sequences spanning 13 days from 11 global positioning system monitoring stations in the Jinduicheng open-pit mine in Shaanxi Province, China, were applied to forecast the slope deformation. The results from both the traditional fractal model and the improved fractal model can accurately forecast the slope deformation value fairly close to the actual field monitoring value, but the latter can make a more accurate prediction than the former. There is a significant relationship between the prediction accuracy and the data sequence dispersion. Further analysis revealed that our improved fractal model is more capable of resisting the volatility existing in the data sequences than the traditional fractal model. These findings assist in understanding the applicability of prediction models and the deformation trends of open-pit mine slopes.

Displacement prediction of open-pit mine slope based on SSA-ELM

“…The number of hidden layers of the ELM model is 6 and the transfer function is sig type through the trial algorithm, and the prediction result of ELM is obtained, and compared with the BP (Sun, 2014;Xie, et al, 2014) and GM (1,1) (Sun, 2014;Sun et al, 2016;Wu, et al, 2015) models, as shown in Figure 2. Analysis of Figure 2 shows that the prediction results of the BP model can predict the trend of displacement, but almost every predicted value deviates from the real value, and the prediction result is the worst; the prediction effect of the GM (1,1) model is better than that of the BP model, but the model predicts Unstable, the predictive effect is also poor.…”

SHM-informed life-cycle intelligent maintenance of fatigue-sensitive detail using Bayesian forecasting and Markov decision process

“…However, it poses a challenge in the practical application of prediction when facing the uncertainty and non-stationary features inherent in the monitoring data. To solve this aspect, different forecasting algorithms have been widely studied and attempted, for instance, the statistical models, 6 time series models, 7 gray system theory, 8 and singular spectrum model. 9 More recently, the Bayesian forecasting model has been applied in real-world data prediction due to its merits.…”

Research on Deformation Prediction of Foundation Pit Based on PSO‐GM‐BP Model

“…e GM (1, 1) model can extract the chaotic data series. Trends, generate new data columns and use them for predictive analysis [13]. Foundation pit deformation is a complex and nonlinear problem.…”