顾及降雨影响的动态优化时滞时序GM(1,2)模型在滑坡位移预测中的应用

滑坡体除了因自身重力产生位移外,还受到降雨的影响,但通常降雨对滑坡位移的作用具有滞后性。为了分析并预测降雨对滑坡位移的影响,本文提出一种顾及降雨影响的动态优化时滞时序GM(1,2)滑坡位移预测模型。首先,利用经验模态分解(EMD)分解位移序列并通过时间序列重构得到周期位移序列和趋势位移序列,对降雨数据和滑坡周期位移序列进行时滞分析和相关分析,确定时滞时间和影响程度,建立基于背景值优化的动态时滞GM(1,2)模型预测降雨量变化导致的滑坡周期位移变化;然后,建立门限自回归模型预测滑坡趋于自然变化的趋势位移;最后,通过时序叠加得到顾及降雨影响的滑坡预测位移,建立了顾及降雨因素的动态优化时滞时序GM(1,2)组合预测方法。本文以福宁高速公路八尺门滑坡和秭归县八字门滑坡监测数据为例,验证了动态优化时滞GM(1,2)模型的精度,并与其他模型的预测结果进行了对比分析。试验结果表明,动态优化时滞时序GM(1,2)组合预测模型能准确地预测降雨影响导致的滑坡位移变化,预测效果较好,该组合模型对滑坡灾害的预警与防治具有一定的参考价值。

Application of dynamic optimization time-delay GM(1,2) model in landslide displacement prediction considering the influence of rainfall

In addition to the displacement caused by its own gravity, the landslide body is also affected by rainfall, but usually the effect of rainfall on the displacement of the landslide has a hysteresis. In order to analyze and predict the impact of rainfall on landslide displacement, this paper proposes a dynamic optimization time-lag time-lag GM(1,2) landslide displacement prediction model that takes into account the impact of rainfall. First, use EMD (empirical mode decomposition) to decompose the displacement sequence and reconstruct the periodic displacement sequence and the trend displacement sequence through the time sequence. Perform time lag analysis and correlation analysis on the rainfall data and the landslide periodic displacement sequence, determine the time lag and the degree of influence, and establish an optimization based on the background value. The dynamic time-lag GM(1,2) model predicts the cyclic displacement change of the landslide caused by the change of rainfall. At the same time, a threshold autoregressive model is established to predict the trend displacement of the landslide tending to natural changes. Finally, the landslide prediction displacement taking into account the influence of rainfall is obtained through time series superposition. Established a dynamic optimization time lag time GM(1,2) combined forecasting method that takes into account the rainfall factor. The paper uses the monitoring data of Funing Bachimen landslide and Zigui county Bazimen landslide as examples to verify the accuracy of the dynamic optimization time-lag GM(1,2) model, and compares and analyzes the prediction results with other models. The experimental results show that the dynamic the optimized time-lag time series GM(1,2) combined forecasting model can accurately predict the landslide displacement changes caused by rainfall, and the forecasting effect is better, the combined model has certain reference value for the early warning and prevention of landslide disasters.