## 4 Experiments
### Taining and Performance Evaluation of the Proposed Model  
To train the proposed model and evaluate its performance, we used as the training data the 19,972,800 (80%) trajectory window data records for the first 292 days in the available data set and as the test data, the remaining 4,993,200 (20%) trajectory window data records for the following 73 days. For the training data, the portions of each fishing gear type were not uniform as follows: purse seine class 21.2% (4,234,234 records), stow nets class 15.7% (3,135,730 records), longline class 12.4% (2,476,627 records), drift gill nets class 9.2% (1,837,498 records), traps class 18.4% (3,674,995 records), and trawls class 23.1% (4,613,717 records). To resolve the class imbalance problem, we randomly selected 1,000,000 records for each fishing gear type at each training epoch. The portions of each fishing gear type in the test data were also not uniform. Hence, we randomly selected 300,000 records for each  
Figure 8: Spatial trajectory distributions for fishing gear types: the black tracks indicate a section with a speed of 6 knots or more and red indicates ones with a speed of less than 6 knots. fishing gear type from the test data as the validation data set. Once the trained model was obtained, we used the entire test data set to evaluate the trained model. The performance of the trained models was evaluated with respect to the proposed performance indices TPI and DPI. In the experiment, the proposed model performance was significantly affected by the size of the trajectory window. Figure 9 shows the performance of each type of fishing gear class when the size of the trajectory window is set to 1, 5, 10, 15, 20 and 25 h, respectively. To evaluate the validity of the proposed method, we also developed an SVM (support vector machine)-based model for the same data set, and compared the performances of the proposed model and the SVM-based model. For the SVM-based model, both course change and speed were discretized into 10 intervals and their histograms were constructed. The histograms and environmental data were used as the input to the SVM-based model. To handle multiple fishing gear classes in the SVM-based model which is a binary classifier, we used the one-versus-rest (OVR) scheme where the classes are fitted against all other classes [30]. There are several kernel functions which can be applied in an SVM model. In the experiments, the following three kernel functions had been examined: sigmoid, polynomial, and linear kernels. For each kernel type, the same experiments had been conducted and the performance of the trained SVM models has been measured. Among the kernel functions, the linear kernel gave the best performance with the hyperparameters of C = 1 and \(\gamma~{}=~{}0.5\). Table 4 shows the performances of the SVM models for different kernel functions. According to the applied kernel function, the SVM models have shown significantly different performances in the experiments. Table 5 shows the TPI scores of the SVM-based model and the CNN-based model for the test data set in which the size of the TW is 24 h. The purse seine, trap, and trawl fishing gear types \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**SVM-Based Fishing Gear Type Identification**} \\ \cline{2-4}  & **Kernel = ’sigmoid’** & **Kernel = ’poly’** & **Kernel = ’linear’** \\ \hline Purse Seine & 0.917 & 0.819 & 0.922 \\ \hline Stow nets & 0.670 & 0.130 & 0.679 \\ \hline Longline & 0.962 & 0.927 & 0.953 \\ \hline Drift gill nets & 0.728 & 0.401 & 0.743 \\ \hline Traps & 0.242 & 0.152 & 0.399 \\ \hline Traps & 0.922 & 0.874 & 0.918 \\ \hline average & 0.740 & 0.525 & 0.769 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The comparative experiments results of the support vector machine (SVM) model kernel. Figure 9: The trajectory window-wise performance index (TPI) performance changes with TW size. have TPI scores of more than 0.92 for the CNN-based model.