耦合注意力机制大坝变形改进LSTM序列到序列预测模型

Improved LSTM Sequence-to-Sequence Prediction Model for Dam Deformation Coupled with Attention Mechanism

目前,大坝变形预测主要采用的浅层网络结构存在难以挖掘数据序列隐含深层特征的问题.常用的LSTM和GRU等模型虽然具有分析变形序列的时间自相关性特征的特点,但忽略了环境因子序列和变形序列之间的映射关系,且难以克服深度神经网络梯度下降训练易陷入局部最优的问题.针对上述问题,提出了耦合注意力机制大坝变形改进LSTM序列到序列预测模型.利用编码和解码双层LSTM构建序列到序列结构,同步提取输入影响因子和输出变形的序列特征,并耦合注意力机制,动态度量各影响因子对变形的贡献率,以提高预测精度.进一步利用蚁群信息素及双混沌优化改进鲸鱼捕食机制,构建基于改进鲸鱼优化算法的耦合注意力机制的LSTM序列到序列网络模型的无梯度环境,规避早熟收敛,弥补梯度下降本身的缺陷.工程应用结果表明,本文所提模型能够精确预测大坝变形,在各点位测试集上平均MAPE、MAE和RMSE分别为0.125%、0.604 mm和0.865 mm.此外,时效、水位和温度分量对点位变形的贡献率依次为51.93%、30.14%和17.93%.本研究为大坝安全监控提供理论与技术支撑.

At present,the shallow network structure which is mainly used for dam deformation prediction is difficult to mine the hidden deep characteristics of data series.Moreover,although the commonly used models such as LSTM and GRU can analyze the temporal autocorrelation characteristics of deformation series,they ignore the mapping relationship between the environmental factor series and deformation series,and it is difficult to overcome the problem that the gradient descent training of deep neural network is easy to fall into local optimums.To solve these problems,an improved LSTM sequence-to-sequence prediction model for dam deformation coupled with attention mechanism was proposed.The sequence-to-sequence structure was constructed by encoding and decoding a double-layer LSTM,and the sequence characteristics of influencing factors for input and output deformation were extracted synchronously.The contribution rate of each influencing factor with respect to deformation was measured dynamically by coupling the attention mechanism to improve prediction accuracy.Furthermore,ant colony pheromones and double-chaos optimization were used to improve the whale feeding mechanism,so as to construct a gradient-free environment from the LSTM sequence-to-sequence network model coupled with attention mechanism based on the improved whale optimization algorithm.In this way,the premature convergence is avoided and the defect of gradient descent itself is corrected.The results of engineering applications show that the proposed model can accurately predict dam deformation.The average MAPE on a test set of different points is 0.125%,and the corresponding average values of MAE and RMSE are 0.604 mm and 0.865 mm,respectively.In addition,the contribution rates of aging,water level and temperature with respect to point deformation are 51.93%,30.14%and 17.93%,respectively.This study provides a theoretical and technical support for dam safety monitoring.