# An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns
## 3 Results
### Distinctive Recognized Patterns  
By clustering the normalized feature importance sequences generated from each experiment, different feature importance sequences can be clustered. The number of clusters for each feature's feature importance sequence is confirmed using the elbow method. According to the elbow theory, as the number of clusters continues to increase, the rate of decrease will tend to stabilize. Thus, we generally select the end value of the steepest segment as the real number of clusters [39]. By visualizing the Euclidean distances obtained from the elbow algorithm at different numbers of clusters, as shown in Figure 5, Figure 5 represents the elbow visualization results of four different experiments, and it can be determined that the optimal number of clusters is 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Clustering Categories** & **Num** \\ \hline Cluster 1 & 54 \\ Cluster 2 & 211 \\ Cluster 3 & 235 \\ Cluster 4 & 318 \\ Cluster 5 & 316 \\ Total & 1134 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The number of events under different cluster categories.  
Comparing the number of categories causing marine heatwaves in the coastal areas of China mentioned in the literature [42] and the number of clusters determined by the elbow method, we find that they match the expert's experience. The normalized feature importance is clustered using the K-Medoids method, and the clustering results are shown in Figure 6.  
To summarize, we conducted an analysis of the experiment with a random seed of 100 and a time step of 180. The statistical results of 1134 marine heatwave events that occurred  
Figure 5: The determination results of the elbow clustering number under different independent experiments. (**a**) presents the results with a random seed of 100 and a time step of 180. (**b**) displays the results with a random seed of 200 and a time step of 180. (**c**) shows the results with a random seed of 100 and a time step of 240. (**d**) depicts the results with a random seed of 200 and a time step of 240.  
Figure 6: The results under different cluster categories, where the first column represents the clustering results of sea surface pressure feature importance, and the second column represents the clustering results of 10m wind speed feature importance. Moreover, in each figure, the darkest curve represents the cluster center of each category.  
at 13 stations from 1982 to 2022 are shown in Table 6, and the number of categories at each station is shown in Figure 7, and the visualization of clustering results on the map is shown in Figure 8. The lighter color represents a greater number of marine heatwave events. From the map, it can be observed that the majority of the first category of marine heatwaves occur along the South China Sea coast, with a few along the other coastal regions of China. The second category predominantly concentrates near the Bohai Strait, the third is focused southwest of the East China Sea and northeast towards the South China Sea, and the fourth category mainly occurs in the East China Sea region. The fifth category primarily takes place near the Bohai Sea and the Yellow Sea.