考虑注意力机制的新型深度学习模型预测滑坡位移

Landslide Displacement Prediction Based on a Deep Learning Model Considering the Attention Mechanism

现有的基于数据驱动的滑坡位移预测模型大多是基于时间序列数据的单点建模,不能考虑整个边坡的变形相关性和滑坡变形的全局建模.为了克服这一缺点,本研究提出了一种基于时空注意(spatial-temporal attention,STA)机制的深度学习模型,该模型将卷积神经网络(convolutional neural network,CNN)与长短时记忆(long short-term memory)神经网络相结合.通过CNN和卷积注意力模块提取滑坡位移的空间变形特征,利用时间注意机制和LSTM模型从外部因素的时间序列数据中捕获重要的历史信息.注意力机制输出的注意权重值可以揭示滑坡变形的时间-空间特征.以三峡库区泡桐湾滑坡为例,对该模型的性能进行了验证.结果表明,STA-CNN-LSTM模型预测的均方根误差(RMSE)和平均绝对百分比误差(MAPE)与传统灰狼算法优化的支持向量机(GWO-SVM)模型相比分别下降了9.28%和13.88%.模型因子权重计算结果表明,在监测期内随着时间的推移,降雨对泡桐湾滑坡变形的影响逐渐增加,而库水位的影响逐渐减小.

Accurate displacement prediction plays an important role in landslide early warning.However,the majority of the existing data-driven models focus on single-point modeling based on time series data which cannot consider the deformation correlation in the whole slope.To overcome this drawback,this study proposed a spatial-temporal attention(STA)mechanism-based deep learning model which combined the convolutional neural network(CNN)with the long short-term memory(LSTM)neural network.A convolutional block attention module(CBAM)combined with CNN was developed to extract the spatial deformation characteristics of the slope.A temporal attention module and LSTM model were used to learn the significant historical information from the input external conditions time series data.The model also allowed to output the tempo-spatial attention weights to reveal the tempo-spatial characteristics of landslide deformation.The Paotongwan landslide with step-like behavior displacement in the Three Gorges Reservoir Area(TGRA)of China was used to validate the model performance.The results show that,the root mean square error(RMSE)and the mean average percentage error(MAPE)of the STA-CNN-LSTM model decreased 9.28%and 13.88%,respectively,compared with grey wolf optimization optimized support vector machine(GWO-SVM).The attention weight results calculated by STA-CNNLSTM demonstrate that rainfall had a larger impact on the deformation of the Paotongwan landslide at the beginning of the monitoring while the influence of reservoir water level decreased with ongoing of the monitoring.