人民黄河2024，Vol.46Issue(12)：127-130,143,5.DOI:10.3969/j.issn.1000-1379.2024.12.021
基于TimeGAN和CNN-BiLSTM-Attention的大坝变形预测混合模型
Hybrid Model for Dam Deformation Prediction Based on TimeGAN and CNN-BiLSTM-Attention
摘要
Abstract
Deep learning models based on historical data often require a large dataset spanning several years.In order to address the issue of insufficient data,a hybrid model for predicting the deformation of concrete face rockfill dams was proposed,which combined Time Series Generative Adversarial Networks(TimeGAN)with CNN-BiLSTM-Attention.Firstly,it used TimeGAN to generate virtual data to expand the sparse dataset.Then,convolutional neural networks(CNN)were used to extract nonlinear local features from dam sensor data,and BiLSTM was used to capture bidirectional time series features.Finally,the attention mechanism was introduced to automatically fit the weight alloca-tion of information features extracted by the BiLSTM layer,and the final prediction result was output through the fully connected layer.Taking a concrete face slab dam in Bijie City of Guizhou Province as an example,it verified the applicability of the hybrid model in practical engi-neering.It established four basic models of Long Short Term Memory Network(LSTM),CNN-LSTM,CNN-LSTM-Attention and CNN-BiL-STM-Attention,and introduced TimeGAN separately to compare the prediction accuracy of each model.The results show that the mixed model based on TimeGAN and CNN-BiLSTM-Attention has significantly better fitting performance than other models,and its predicted values are closest to the monitored values.Compared to traditional single LSTM models,its EMS,ERMS and EMA are reduced by 71%,49%and 45%respectively,and R2 is improved by 20%.关键词
TimeGAN/CNN/BiLSTM/Attention/混凝土面板堆石坝/变形预测Key words
TimeGAN/CNN/BiLSTM/Attention/concrete face rockfill dam/deformation prediction分类
建筑与水利引用本文复制引用
原佳帆,李丹杨,李佳霖,秦学,毛鹏..基于TimeGAN和CNN-BiLSTM-Attention的大坝变形预测混合模型[J].人民黄河,2024,46(12):127-130,143,5.基金项目
贵州省科技计划项目(黔科合支撑[2023]一般251) （黔科合支撑[2023]一般251）
贵州省基础研究计划(自然科学)青年引导项目(黔科合基础[2024]青年095) （自然科学）
