## 5 Results
### Anchor Position Detection  
The data of Tables 4 and 5 were input into the MATLAB simulation platform, and the two-dimensional coordinates of anchor position distribution that conforms to the safety distance of the target ship were detected by the mooring area detection model and the Monte-Carlo stochastic simulation method. Then, using the Plot function, the coordinates of the anchor position distribution of the target ship were drawn into a two-dimensional image. Additionally, 10 groups of data in this experiment generated 60 two-dimensional images of anchor position detection. Moreover, Figures 9-12 provide eight anchor position detection images that were randomly extracted. Further, the black dots in Figures 9-12 simulate existing ships or obstacles. Meanwhile, the color red represents the available anchor positions distribution that meets the detection algorithm of mooring area detection. The color blue represents the available anchor positions distribution for ships with an additional 50 m of safety distance. Pink represents the available anchor positions distribution for ships with an additional 100 m of safety distance. Yellow represents the available anchor positions distribution for ships with an additional 100 m safety distance. In Figure 12, due to the existing ships or the congestion of obstacles in the anchorage area, it is impossible to detect the anchorage position required by the larger ship size, so the corresponding colored location distribution of the anchorage area cannot be plotted. Figure 10: Schematic diagram of mooring area detection with wind force >7 and water depth of 20 m. (**a**) Represents 192 m and 192 m ship type mooring area detection. (**b**) Represents 333 m and 333 m ship type mooring area detection. Figure 9: Schematic diagram of mooring area detection with wind force \(\leq\)7 and water depth of 20 m. (**a**) Represents 192 m and 192 m ship type mooring area detection. (**b**) Represents 333 m and 333 m ship type mooring area detection. Figure 11: Schematic diagram of mooring area detection with wind force \(\leq\)7 and water depth of 40 m. (**a**) represents 192 m and 192 m ship type mooring area detection. (**b**) represents 333 m and 333 m ship type mooring area detection. Figure 12: _Cont._  
The conclusions of this study are as follows:  
1. The intelligent MASS mooring area detection algorithm based on the improved anchorage area radius model and the Monte-Carlo stochastic simulation method sampled a total of 5000 times and required 1.1 s to output the detection results. The experimental results showed that the detection algorithm can accurately and efficiently detect the distribution of anchor positions within the anchorage boundary. 2. The intelligent MASS mooring area detection algorithm fully considered the influence of the ship type on the anchoring distance, and provided a reasonable mooring area detection scheme for ships with hazardous items such as oil and liquefied gas. 3. The intelligent MASS mooring area detection algorithm fully considers the safety needs of ships passing through the anchorage with a certain safety distance. 4. The intelligent MASS mooring area detection algorithm fully considers the influence of the anchorage boundary. The tail of the target ship can effectively avoid the anchorage boundary and prevent the anchoring ship from floating out of the anchorage boundary. 5. The intelligent MASS mooring area detection algorithm can flexibly increase the safety distance as the circumstances may require. In this experiment, the distance of 50 m was adopted as the super parameter distance so as to flexibly adjust the safety distance of anchoring ships, as shown in Figures 9-12.  
information for each ship, and generate optimal mooring locations in the future. The system can be also directed by the port authority to manage the anchoring operation of a ship. However, only 192 m, 225 m and 333 m bulk carriers were selected as case studies in this study, while research on ships of less than 200 m in length is lacking.