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受库水位涨落及降雨等影响,库区滑坡位移表现出明显的周期性。基于位移时间序列分析,将滑坡监测位移分解为趋势项与周期项之和。趋势项反映滑坡变形的长期趋势,其主要受滑坡本身地质结构等因素影响。周期项反映滑坡变形的波动性,其主要受外部因素影响。以三峡库区巫山塔坪滑坡为例,考虑长江水位与降雨量影响,采用H-P滤波法从滑坡位移中分解出趋势项及周期项,利用差分自回归滑动平均模型(ARIMA)对趋势项进行平稳处理并计算趋势项预测值,利用向量自回归模型(VAR)计算周期项预测值。趋势项预测值与周期项预测值之和为滑坡位移预测值。与实际监测值及多种方法分析比较,表明综合预测所得结果能较好反映滑坡变形的趋势性和波动性,位移预测效果较好。
Affected by the fluctuation of reservoir water level and rainfall, the landslide displacement in the reservoir area shows obvious periodicity. Based on the displacement time series analysis, the displacement of landslide monitoring is decomposed into the sum of the trend and period. Trend items reflect the long-term trend of landslide deformation, which is mainly affected by the geological structure of the landslide itself and other factors. Periodic items reflect the variability of landslide deformation, which is mainly affected by external factors. Taking the Tashan Landslide in the Wushan Reservoir area of the Three Gorges as an example, considering the effects of the water level and rainfall in the Yangtze River, the trend and periodical terms are decomposed from the landslide displacement using the HP filter method. The trend of the trend is stabilized by the ARIMA Processes and calculates the forecast of trend items, and calculates the forecast of period items by using vector autoregressive model (VAR). The sum of the forecast value of the trend item and the forecast value of the period item is the predictive value of the landslide displacement. Compared with the actual monitoring value and a variety of methods, it shows that the comprehensive prediction result can better reflect the trend and fluctuation of landslide deformation, and the displacement prediction is better.