为提高白水河滑坡位移预测精度,提出一种新的预测模型,即基于自适应噪声完全集合经验模态分解(CEEMDAN)-蝙蝠算法(BA)-支持向量回归机(SVR)-自适应提升算法(Adaboost)的模型。以该滑坡为研究对象,利用CEEMDAN将滑坡位移分解为趋势项以及由IMF分项构成的波动项。首先采用BP神经网络对趋势项位移进行预测,随后利用CEEMDAN-BA-SVR-Adaboost模型对波动项进行预测,并将预测结果与CEEMDAN-PSO-SVR-Adaboost、CEEMDAN-BA-BP-Adaboost、CEEMADAN-BA-SVR、BA-SVR-Adaboost模型预测结果进行对比分析,验证本模型在位移预测方面的优越性。此外,利用CEEMDAN-BA-SVR-Adaboost模型对ZG118波动项位移进行预测,同时计算ZG93监测点最终累计预测位移。结果表明,对白水河滑坡位移进行预测时,CEEMDAN-BA-SVR-Adaboost模型具有较高的准确性和适用性。
Abstract
To improve the accuracy of predicting the displacement of Baishuihe landslide, we present a prediction model combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), bat algorithm (BA), support vector regression machine (SVR), and Adaptive Boosting (Adaboost). By using CEEMDAN, we decompose the displacement of Baishuihe landslide into trend term and fluctuation term composed of IMF sub-items. BP neural network is used to predict the trend term displacement, and the CEEMDAN-BA-SVR-Adaboost model to predict the fluctuation term. We further compare the present prediction result with those of CEEMDAN-PSO-SVR-Adaboost, CEEMDAN-BA-BP-Adaboost, CEEMADAN-BA-SVR, as well as BA-SVR-Adaboost models to verify the superiority of the present method in displacement prediction. We employ the CEEMDAN-BA-SVR-Adaboost model to predict the fluctuation term displacement of monitoring point ZG118 while calculating the final cumulative predicted displacement of monitoring point ZG93. Results verify that the CEEMDAN-BA-SVR-Adaboost model has good accuracy and applicability in predicting the displacement of Baishuihe landslide.
关键词
白水河滑坡 /
位移预测 /
自适应噪声完全集合经验模态分解(CEEMDAN) /
蝙蝠算法(BA) /
支持向量回归机(SVR) /
集成学习
Key words
Baishuihe landslide /
displacement prediction /
complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) /
bat algorithm (BA) /
support vector regression machine (SVR) /
Adaboost
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基金
国家自然科学基金项目(41372306, 41502299);成都理工大学青年骨干计划项目(KYGG201720)
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