传统GM(1,1)预测模型在大坝位移拟合及预测中存在优化方式单一、适应性不佳等不足,一定程度上影响模型的预测效果。基于蛙跳算法,通过优化背景值和平滑系数、寻找最优定解条件以及残差优化等方法,提出了改进的非等间距GM(1,1)大坝位移预测模型。结合相关工程实例,对比分析了2种模型的拟合效果和预测精度,说明了相对于传统GM(1,1)大坝位移预测模型,改进的GM(1,1)大坝位移预测模型能有效提高位移预测精度,可以应用于实际大坝结构中的位移监控及预测。

Traditional GM(1,1) forecasting model has such deficiencies as single optimization method and poor adaptability in fitting and estimating dam displacement, which affect the estimation result. In this paper, an improved non-equidistance GM(1,1) forecasting model is proposed by using Suffled Frog Leaping Algorithm to optimize background value and smoothing coefficient, to search for optimal definite condition and correct residual error. The fitting results and prediction accuracy of traditional model and the proposed model are compared through an engineering example application. Results suggest that the improved GM(1,1) model could enhance the prediction accuracy effectively, hence can be used in the monitoring and prediction of dam displacement.