Unbiased Grey Fuzzy Markov Research On Slope Displacement Forecasting

Slope deformation and displacement is reflected important information, the accuracy of prediction model a direct impact on the future of slope deformation value judgments, to improve the accuracy of the model is to obtain an accurate prediction deformation values important prerequisite to analyze the resulting displacement monitoring, predictive models can be combined discover patterns and trends slope deformation and deformation to predict the future value of the slope, slope stability analysis is instructive.In this paper, the theoretical analysis as a starting point, choose the traditional gray theory is the basic theory and on this basis, combined with related disciplines theory to construct a new unbiased gray fuzzy Markov prediction model, to improve the prediction accuracy of the model. Through on-site extraction of data analysis to validate predictive models and concluded as follows:(1) From the perspective of the system, examine the slope displacement of complexity and randomness, select the gray forecasting system to predict the slope for complex systems, a clear model of gray system theory is a description of some specific parameters, and predicted the existence of bias results errors.(2) On the basis of gray prediction model, to build a combination forecasting model, using unbiased gray theory to eliminate bias, the integration of fuzzy set classification, Markov theory, the predicted results residuals corrected.(3) the experimental area collected by the data input predictive model, the predicted values obtained were compared with the true value of the average relative error, compared with the traditional gray forecasting model, high accuracy Unbiased gray fuzzy Markov prediction model.Articles proposed prediction method for the study of slope prediction method provides a new way of thinking, and combined with field experiments to verify the prediction method, we can see this kind of technology route is feasible. While providing a more valuable reference for slope stability analysis. Compared with the existing prediction model Unbiased gray fuzzy Markov model predictions, residuals small, high precision, and model building is relatively simple, practical, strong.