# Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction
## 4 Discussion  
Compared to the traditional EKF method, CNN-GRU demonstrates significant advantages in GNSS deformation monitoring. These are primarily manifested in its superior capability for nonlinear modeling, stronger spatiotemporal correlation, enhanced data representation and learning, as well as higher prediction accuracy and reliability. By effectively capturing the nonlinear relationships and spatiotemporal correlations within the data, the CNN-GRU method can provide more precise and reliable predictions for deformation monitoring, thus ensuring greater safety and stability in hydraulic engineering projects. The experiment shows that the CNN-GRU method has about 76% improvement against the EKF method.  
The CNN model extracts features from the GNSS time series by stacking convolutional layers, pooling layers, and fully connected layers, gradually combining these features for prediction. It has strong feature extraction capabilities and translation invariance. However, it remains sensitive to changes in the scale and position of the target and requires large amounts of data. This study carries on experiments with only the ReLU activation function and the Adam optimizer. In the future, more activation functions and optimizers will be tested to accelerate the model convergence and improve its performance.  
The GRU model improves the training effectiveness by introducing gate mechanisms to address issues like gradient vanishing and exploding in traditional RNN models. However, its ability to model long-term dependencies is limited, and it does not fully exploit the information in the middle part of the GNSS time series. Introducing attention mechanisms in the future will enable the model to better focus on important parts of the GNSS time series, enhancing its ability to handle information in the middle of the GNSS time series.  
Figure 16: The prediction diagram of the CNN-GRU and EKF method.  
The proposed CNN-GRU model utilizes the CNN model to extract features from the GNSS time series and the GRU model to capture long-term dependencies, achieving better performances in modeling. It implements hierarchical feature learning, effectively extracting features from GNSS time series and capturing long-term dependencies, thus enhancing the model's ability for GNSS time series. The proposed method has four advantages:  
1. The proposed method comprehensively utilizes spatial and temporal features, enabling a more thorough analysis of the data;
2. The proposed method reduces information loss and enhances model performance;
3. The proposed method can more accurately capture spatial and temporal features of the data, thereby improving prediction accuracy;
4. The proposed method combines the advantages of both models, enabling better balance between model complexity and generalization ability, thus reducing the risk of overfitting.  
However, parameter tuning is complex, and training is time-consuming. In the future, structural optimization and attention mechanisms will be introduced to enable the model to better focus on important parts of the GNSS time series, improving model performance.