基于EEMD-Prophet-LSTM的滑坡位移预测

Prediction of landslide displacement based on EEMD-Prophet-LSTM

对于阶跃型滑坡位移这一非稳态自然过程,提出一种结合集合经验模态分解法(EEMD)、Prophet和长短时记忆网络(LSTM)的滑坡位移预测方法。以白水河滑坡位移数据为例,采用EEMD将位移时间序列分解为若干个本征模态函数(IMF)和残差(RES),将包含周期因素、随机因素的IMF叠加视为波动项,RES视为趋势项。分别采用Prophet和LSTM预测趋势项与波动项,两项预测结果叠加得到滑坡位移预测值。结果表明:该方法对于少量数据的白水河滑坡位移预测拟合度(R^(2))达到0.98以上,优于支持向量机、人工神经网络等传统机器学习方法。且此方法对八字门滑坡各监测点的预测精度R^(2)同样在0.96以上,证明了此方法的有效性。

For the unsteady process of step-type landslide displacement,a method combining ensemble empirical mode decomposition(EEMD),Prophet,and long short time memory network(LSTM)to predict landslide displacement is proposed.The displacement data of Baishuihe landslide was taken as examples.The displacement time series was decomposed into residual(RES)and several intrinsic mode functions(IMF)by EEMD.The superimposition of IMFS which included periodic factors and random factors was considered as a volatility item,and the RES was regarded as a trend term.The trending term was fitted by the Prophet and the the volatility term was predicted by LSTM.The addition of the two prediction results was the predictied value of the landslide displacement.The results show that the coefficient of determination(R^(2))of the EEMD-Prophet-LSTM model is above 0.98 for Baishuihe landslide displacement prediction,which is better than traditional machine learning methods such as support vector machine and artificial neural network.Moreover,the prediction accuracy R^(2)of this method for each monitoring point of the Bazimen landslide is also above 0.96,which proves the applicability of this method.