## 4 Results  
This section outlines the clustering results of the submarine landslide susceptibility using k-means, spectral, and hierarchical models after parameter validation and selection. The study area was divided into four areas of different submarine landslide susceptibilities without real geological labels. To define the final labels, all the geological parameters should be considered. As mentioned in Section 3.2, the hydrodynamic force to the seabed increases first with the deepening of water depth and then decreases. Moreover, the sediment type changes from silt to clay, which is difficult to be influenced, when the water depth is deeper than 15 m. Therefore, the submarine landslide susceptibility labeling principle of this area is that: the least serious part is the clay region; the second least serious part is the shallow water region; the most serious part is around the 10 m bathymric contour; and the second most serious part is beside or around it. As shown in Figure 5, the study area was divided into four parts of submarine landslide susceptibility using the k-means model. The very high-susceptibility part is located at a water depth of 5-11 m, and the edge is a discrete distribution. The high-susceptibility part is distributed at a water depth of 11-13 m. The low-susceptibility part is situated at a water depth of less than 5 m. The very low-susceptibility part is located at a water depth deeper than 13 m. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Cluster Number** & **Gamma** & **Affinity** & **Linkage** \\ \hline k-means & 4 & None & None & None \\ Spectral & 4 & 1 & None & None \\ Hierarchical & 4 & None & Manhattan & Average \\ \hline \hline \end{tabular} The parameters are not necessary for the model when it shows ”None”.  
\end{table} 
Table 1: Parameters used in different models. Figure 5: Distribution of submarine landslide susceptibility using k-means. The different colors represent different submarine landslide susceptibilities. It can be seen from Figure 5 that the study area was divided into four parts of submarine landslide susceptibility using the spectral clustering model. The very high-susceptibility part is located at a water depth of 5-12 m. The high-susceptibility part is distributed at a water depth of 12-15 m. The low-susceptibility part is situated at a water depth of less than 5 m. The very low-susceptibility part is located at a water depth deeper than 15 m. Compared with the results obtained by k-means, the distribution of submarine landslide susceptibility using spectral clustering is more continuous than that using the k-means algorithm. Furthermore, the very high-susceptibility part is wider and the very low part is narrower in Figure 6 than in Figure 5.  
As shown in Figure 7, the study area was divided into four parts of submarine landslide susceptibility using the hierarchical clustering model. The very high-susceptibility part is located at a water depth of 5-8 m. The high-susceptibility part is distributed at a water depth of 8-13 m. The low-susceptibility part is situated at a water depth of less than 5 m, and the very low-susceptibility part is located at a water depth deeper than 13 m. By comparing the results of the three algorithms, we can find that the very high-susceptibility part obtained by using the hierarchical clustering model is much less than other two of the other methods. Generally, the low- and very low-susceptibility parts of these three methods are very close. The main differences are in the size and distribution of very high and high areas. The three unsupervised machine learning methods obtained two main common parts: one is a low-hazard region at shallow depths of 5 m; the other is a very low-hazard region at depths of 13 m. The reason for this phenomenon is that the hydrodynamic conditions are relatively weak in these two parts of the area, and the influence of various geological-impact parameters on submarine landslides is also small; thus, the results obtained by different algorithms are more consistent.