## 3 Experiments and Result Analysis
### Analysis of Experimental Results
#### 3.2.1 Model Evaluation  
Figures 11 and 12 show the visualization of the 20,000 entries in the cleaned dataset. Each entry contains the position information (longitude, latitude, depth), physical attitudes (pitch angle, yaw angle, roll angle), and time of the AUV. Of these, 80% were used as the training set, and the remaining 20% were used as the testing set. From the figure, it can be seen that among all the data attributes, longitude and latitude vary relatively smoothly over some time, whereas depth, pitch angle, yaw angle, and roll angle vary more, but all the data are not periodic as well as regular. During the experiments, this model was first employed to predict AUV trajectories and was compared with other models (BiLSTM, DBiLSTM). Secondly, the model was utilized to forecast trajectories of AUVs in different marine areas and with various models. In this paper, the experimental model is analyzed through comparative experiments. Based on the results obtained from these experiments, it can be concluded that the model proposed in this paper achieves significant results in predicting AUV trajectories. A comparative study of the experimental findings demonstrates that the performance of the BiLSTM and DBiLSTM models is lower than that of the AWOA-DBiLSTM model. Figures 13-15 show the trajectory prediction results for latitude, longitude, and depth by the BiLSTM, DBiLSTM, and AWOA-DBiLSTM models. The trajectory schematic diagram of depth prediction reveals that, particularly when the data are unstable, the prediction performance of BiLSTM is relatively poor, especially during periods of significant data oscillation amplitude. However, DBiLSTM and AWOA-DBiLSTM still achieve favorable prediction results in unstable data conditions. These results indicate that better prediction can be achieved by extracting data features across different layers for high-dimensional data. In addition to lowering the dimensionality of the data, AWOA-DBiLSTM separates the data features, extracts features layer by layer from the partitioned data features, and filters the data features using a gating mechanism to lessen the impact of irrelevant data features. In addition, the experimental results demonstrate that the experimental performance of DBiLSTM with AWOA is superior to that of DBiLSTM without AWOA. In addition, the experimental results show that the practical performance of DBiLSTM with AWOA is superior to that of DBiLSTM without AWOA. Overall, the AWOA-DBiLSTM model is superior to that of DBiLSTM without AWOA. Figure 11: The preprocessed position data of the AUV: (**a**) presents the visualization of longitude data. (**b**) presents the visualization of latitude data. (**c**) presents the visualization of depth data. demonstrates excellent performance in predicting nonlinear sequential data. It mitigates the limitations associated with relying on mathematical and physical models for prediction to a certain extent. The data-driven neural network model proves feasible for AUV prediction. Partitioning data into features and filtering noise information further enhances the trajectory prediction of AUVs. Figure 12: The preprocessed physical state data of the AUV: (**a**) presents the visualization of yaw data. (**b**) presents the visualization of pitch data. (**c**) presents the visualization of roll data. Figure 14: The trajectory of the AUV is predicted using the DBiLSTM model: (**a**) represents the prediction of latitude by DBiLSTM. (**b**) represents the prediction of longitude by DBiLSTM. (**c**) represents the prediction of depth by DBiLSTM. Figure 13: The trajectory of the AUV is predicted using the BiLSTM model: (**a**) represents the prediction of latitude by BiLSTM. (**b**) represents the prediction of longitude by BiLSTM. (**c**) represents the prediction of depth by BiLSTM. Table 1 clarifies that the suggested AWOA-DBiLSTM performs better than the other models and produces accurate prediction results.