## 3 Experiments and Results
### Experimental Analysis  
In this experiment, the results of different prediction models in the deep ocean layer and the thermocline were compared and analyzed. In addition to the uncorrected LSTM-based prediction model in our framework, the other time series prediction models with better prediction effect in deep learning fields are also selected, which are LSTNet, Transformer, RNN, and LightGBM. Each of the above models uses the same training set and test set. In this experiment, the test set is the track sequence dataset of the deep ocean layer, and the experimental comparison is carried out under the same conditions.  
1. Prediction results of drift track of models in the deep ocean layer  
In the deep ocean layer, 500 track sequences are randomly selected as the test set. Regarding the comparison between the prediction results obtained by each model on the test set and the real track sequence of latitude, longitude, and depth, the result is shown in Figures 11 and 12.  
Figure 11 shows the AUV drift track prediction results of Latitude and Longitude based on ECRNet, RNN, LSTM, LSTNet, Transformer, and LightGBM, respectively. _real_  
Figure 10: AUV real drift track diagram.  
respectively. In addition, in order to show the advantages of our model more directly, we also express the prediction performance of each model through the relative error index. The minimum relative error of our model is 0.0251%. Therefore, the modified neural network in this paper has greatly improved the prediction accuracy of the prediction model in the deep ocean layer, and better fitted the AUV drift track.  
The prediction results of craft track of models in the thermocline were compared with the real track sequence, the results of the Latitude and Longitude are shown in Figure 13, and the results of depth are shown in Figure 14. Owing that \(z\geq-200\), the results of the base prediction model were modified by the Response Modulation module. Figure 13 shows that _real_ and ECRNet achieve the best fitting effect again based on our model. Besides, Figure 14 also shows that the track prediction result of depth based on the ECRNet obtains the best fitting effect. The above results prove that the Response Modulation module implements the feature drift problem for different ocean layers and ensures more accurate and continuous prediction of the AUV drift track.  
Table 2 shows the prediction performance of the above six models when the feature drift problem occurs after the AUV enters the thermocline. In Table 2, the prediction results corrected by the Response Modulation Module have a greater improvement in prediction performance than other models.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model Type** & **Relative Error (\%)** & **MSE (10\({}^{-3}\))** & **RMSE (10\({}^{-3}\))** & **MAE (10\({}^{-3}\))** \\ \hline ECRNet (Our Method) & 0.0251 & 2.167 & 1.472 & 1.369 \\ \hline RNN & 0.1643 & 9.418 & 3.069 & 2.923 \\ \hline LSTM & 0.1026 & 5.651 & 2.377 & 2.164 \\ \hline Transformer & 0.0863 & 4.002 & 2.013 & 1.981 \\ \hline LSTNet LightGBM & 0.0635 & 2.042 & 2.836 & 2.653 \\ \hline \end{tabular}
\end{table}
Table 1: Prediction performance of different models in deep ocean layer.  
Figure 13: Comparison diagram of Latitude and Longitude between the predicted results of each model in the thermocline and the real track sequence.  
Compared with the results before correction, the three evaluation indicators were improved by 13.672, 3.306, and 2.373, respectively. Besides, the ECRNet obtains the better relative error, which is 0.0667%. Therefore, the influence of feature drift on the prediction accuracy is greatly alleviated, and the prediction performance of the model is still guaranteed after entering the thermocline with more accurate prediction results.