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Exponential smoothing method based on wavelet transform for slope displacement prediction

机译:基于小波变换的斜率变换的指数平滑方法

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It has important significance in engineering to analyze rock slope's evolution rule and forecast its development trend based on the safety monitoring displacement data. The actual slope monitoring sequence is non-stationary time series containing a number of errors, therefore, firstly discrete stationary wavelet transform (DSWT) are used to denoising for monitoring data, then the reconstruction series are transformed into a stationary sequence by first-order difference, finally exponential smoothing method is used to prediction for the stationary differential sequence. The combination forecasting model is applied to high slope displacement prediction on the left bank of Jinping I Hydropower Station, the calculation results show that the combined model have higher forecast accuracy compared with other prediction methods, most of the relative errors of the prediction results are less than 5%, meeting engineering prediction requirements.
机译:它对工程有重要意义,分析了摇滚斜坡的演化规则,并根据安全监测排量数据预测其发展趋势。实际的斜率监测序列是包含多个错误的非静止时间序列,因此,首先是离散的静止小波变换(DSWT)用于去噪进行监视数据,然后通过一阶差异转换为固定序列的重建系列,最后指数平滑方法用于预测静止差分序列。组合预测模型应用于金平I水电站左岸的高斜率位移预测,计算结果表明,与其他预测方法相比,组合模型具有更高的预测精度,预测结果的大多数相对误差较小比5%,满足工程预测要求。

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