(1 School of Architecture and Civil Engineering，Xiamen University，Xiamen，Fujian 361005，China；2. Changsha Institute of Mining Research Co.，Ltd.，Changsha，Hunan 410012，China)
Abstract：Considering the traditional GM(1，1) model in slope deformation prediction can not reflect the impact of external environmental changes on the trend of system change and inherent structural shortcomings in GM(1，N) model，OGM(1，N) model was introduced and its background value is optimized by adaptive particle mutation swarm optimization according to the error of background value in gray system. Three monitoring points(G7，G8，G9) on the waste dump were analyzed by grey relational analysis theory. Then the improved OGM(1，N)model was used to predict the waste dump deformation in Jiepailin mine. Compared the results with GM(1，1) model，Verhulst model and Nonlinear regression，the results of three monitoring points showed that the improved OGM(1，N) model is more precise than the other three models. In detail，the average relative percentage error of improved OGM(1，N) model is 0.16%，2.26%，10.562%，respectively. The average relative percentage error of GM(1，1) model is 16.57%，18.07%，19.095%，respectively；the average relative percentage error of Verhulst model is 4.52%，2.34%，29.809%，respectively，the average relative percentage error of nonlinear model is 11.44%，8.45%，11.621%，respectively. Comparing the result of the OGM(1，N) model before and after optimization，the average relative simulation percentage error after optimization was reduced to about more than 1/3 of that before optimization. According to the prediction results of three monitoring points，the improved OGM(1，N) model is adaptive and effective in slope deformation prediction.
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2020, Vol. 39 
