基于EEMD-LSTM-ARIMA的土石坝渗压预测模型研究

渗压监测是土石坝渗流安全评价的重要内容之一。由于渗压受到诸多外界因素的影响，测点的渗压值时间序列往往存在非平稳性、局部突变等特点，为此基于“分解-重构-组合”的思想构建了土石坝渗压预测的EEMD-LSTM-ARIMA模型。首先采用集合经验模态分解(EEMD)对时间序列特征进行提取，根据长短期记忆神经网络(LSTM)对提取出的特征分量进行预测，同时结合差分自回归移动平均方法(ARIMA)进行残差修正，组合LSTM和ARIMA的预测结果，重构得到改进预测模型。以某深厚覆盖层上的土石坝工程为例，选取主河床坝体防渗墙后2个典型测点的实测渗压值序列为研究对象进行应用验证。结果表明：相较于单一的LSTM模型和ARIMA模型，改进模型的平均绝对误差MAE、均方误差MSE、均方根误差RMSE均为3种模型中的最小值，预测精度明显优于另外2种模型，该模型为土石坝渗压的精确预测分析提供了新途径。

Seepage pore water pressure prediction model of earth-rock dams based on EEMD-LSTM-ARIMA

Seepage monitoring is one of the important contents of seepage safety evaluation of earth-rock dams. The seepage pore water pressure is affected by multiple external factors, so the time series of seepage pore water pressure at measuring points is often characterized by nonstationarity and local abrupt changes. Regarding to this, the EEMD-LSTM-ARIMA model for seepage pore water pressure prediction of earth-rock dams is constructed according to the concept of decomposition-reconstruction-combination. Firstly, the time series features are decomposed by the ensemble empirical mode decomposition (EEMD), and the extracted feature components are predicted by the long short-term memory (LSTM) neural network. At the same time, the residual error is corrected by the autoregressive integrated moving average (ARIMA), and the improved prediction model is reconstructed by combining the prediction results of LSTM and ARIMA. Taking an earth-rock dam on a deep overburden as an example, the measured seepage pore water pressure series of two typical measuring points behind the cutoff wall of the main riverbed dam are selected as the research objects for application verification. The results show that, compared with the single LSTM model and ARIMA model, the mean absolute error, the mean square error and the root mean square error of the proposed prediction model are the smallest, and the prediction accuracy of the proposed model is obviously superior to the other two models. Therefore, the proposed model can provide a new approach for accurate prediction and analysis of seepage pore water pressure of earth-rock dams.