大坝变形是水压、温度等多种因素综合作用的结果,变形监测数据是非平稳非线性的时间序列,并且在时间维度上具有关联性。为充分挖掘变形监测数据在长短时间跨度上的关联性,提出了应用长短期记忆网络(LSTM)预测大坝变形的方法。为进一步提升预测精度,利用自回归差分移动平均模型(Arima)对预测残差进行误差修正,从而建立基于LSTM-Arima的大坝变形组合预测模型。以某混凝土重力坝为例,将组合模型的预测结果与Arima模型、支持向量机(SVM)的预测结果进行对比分析。结果表明LSTM-Arima的预测结果优于Arima模型和SVM的预测结果,LSTM-Arima的均方根误差(RMSE) 比Arima模型和SVM分别降低了40.65%和59.00%,平均绝对误差(MAE)分别降低了35.49%和55.60%,表明LSTM-Arima模型具有较高的预测精度。研究成果对于更精确地开展大坝变形预测有一定参考价值。
Abstract
Dam deformation is the result of combined action of water pressure, temperature and other factors. The dam deformation monitoring data series is non-stationary and non-linear, and also correlated in time dimension. To fully mine the association of deformation monitoring data in long- and short-time span, the long- and short-term memory (LSTM) is proposed to predict dam deformation in this paper. To further improve the precision of prediction, the autoregressive integrated moving average (ARIMA) model is used to correct the residual sequence of LSTM prediction. The LSTM-ARIMA prediction model of dam deformation is thus established. With a concrete gravity dam as a case study, the predicted results of LSTM-ARIMA model is compared and analyzed against the predicted results of ARIMA model and Support Vector Machine (SVM). The result demonstrates that the LSTM-ARIMA model outperforms ARIMA and SVM, with the root mean squared error (RMSE) falling by 40.65% and 59.00%, respectively, and meanwhile the mean absolute error(MAE) by 35.49% and 55.60%, respectively, which implies that the LSTM-ARIMA model has high prediction accuracy.
关键词
大坝变形预测 /
长短期记忆网络(LSTM) /
Arima模型 /
LSTM-Arima组合模型 /
预测精度
Key words
prediction of dam deformation /
long- and short-term memory(LSTM) /
ARIMA /
LSTM-ARIMA combinatorial model /
prediction accuracy
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基金
国家重点研发计划项目(2018YFC1508603,2016YFC0401601);国家自然科学基金项目(51579086,51739003)
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