大坝变形预测的最优因子长短期记忆网络模型

面对海量的大坝安全监测数据，快速合理地确定大坝变形预测模型的变量因子能够有效提高模型预测的效率和精度。为此，本文提出一种基于最小绝对值收缩和选择算子（least absolute shrinkage and selection operation,LASSO）变量选择和长短期记忆（long short-term memory,LSTM）网络的大坝变形预测模型。首先，通过大坝变形机理分析确定影响大坝变形的相关影响因子集。然后，通过LASSO算法剔除不显著的因子，筛选出最优影响因子作为模型输入变量，并利用LSTM网络建立大坝变形预测模型。最后，以皂市水利枢纽工程的碾压混凝土重力坝为例，对本文方法进行了验证和讨论。结果表明，本文方法具有较高的预测精度，其平均绝对误差（MAE）、均方误差（MSE）与均方根误差（RMSE）均相对较小；与常规预测模型相比，基于LASSO算法的变量选择使模型建立过程更加简单高效，有利于海量监测数据的处理分析。

Optimal factor set based long short-term memory network model for prediction of dam deformation

To handle the massive dam safety monitoring data, quick and reasonable determination of the variable factors of a dam deformation prediction model can effectively improve prediction efficiency and accuracy. This paper constructs a dam deformation prediction model by combining the least absolute shrinkage and selection operation (LASSO) variable selection and a long short-term memory (LSTM) network. First, a set of relevant influencing factors of dam deformation is determined through analysis of the dam deformation mechanism; then, the LASSO algorithm is used to remove the insignificant factors and select the optimal influencing factors as the model input variables, and a dam deformation prediction model is constructed using the LSTM network. Finally, this new method is verified and discussed with application to a case study of a roller compacted concrete gravity dam of Zaoshi Water Control. The results show it improves the accuracy significantly with relatively small mean absolute errors (MAE), mean square errors (MSE) and root mean square errors (RMSE). Compared with the conventional model, its variable selection based on the LASSO algorithm makes the model construction simpler and more efficient, and thus it is conducive to processing and analysis of massive dam monitoring data.