# Dynamic Multi-Period Maritime Accident Susceptibility Assessment Based on AIS Data and Random Forest Model

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## Abstract

**:**

## 1. Introduction

## 2. Materials

#### 2.1. Study Area

^{2}) is located on the coast of China, including most of the East China Sea and South China Sea (Figure 1). It borders Zhejiang, Fujian, Guangdong, Guangxi, and Hainan provinces. This area is an important sea passage between the Northwest Pacific and Uttara Patha Ocean. In addition, the area is also faced with a high maritime accident risk.

#### 2.2. Maritime Accident Data

#### 2.3. Accident-Influencing Factors

#### 2.3.1. Ship Features

#### 2.3.2. Static Environmental Features

#### 2.3.3. Dynamic Weather Features

## 3. Methodology

#### 3.1. Random Forest Model

#### 3.2. Generation of Feature Matrixes

#### 3.3. Feature Selection

#### 3.4. Construction of Training and Testing Datasets

#### 3.5. Evaluation Metrics

## 4. Results

#### 4.1. Correlation Analysis of Explanatory Factors

#### 4.2. Model Performance Analysis

#### 4.3. Generation of Accident Susceptibility Maps

#### 4.3.1. Generation of Accident Susceptibility Maps

#### 4.3.2. Generation of Accident Susceptibility for Blind Data

#### 4.4. Influencing Factor Analysis

## 5. Discussion

#### 5.1. Cost–Benefit Analysis

#### 5.2. The Limitations of This Study

## 6. Conclusions

- The results showed good performances according to the accuracy, recall, precision, F1- measure, ROC, and AUC values in the testing data and blind data;
- In addition, the monthly, yearly, and five-yearly susceptibility maps show similar patterns. The high-susceptibility areas are close to the shore, especially from the Shanghai shore to the Guangxi shore;
- Meanwhile, the conditioning factors in the three models had similar sorting. The ship density and bathymetry were the most critical factors in the three models, contributing around 25% and 20% of the total information.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

Variable | Description |
---|---|

AIS | Automatic Identification System |

RF | Random forest |

A | Accuracy metric |

R | Recall metric |

P | Precision metric |

F1-m | F1-measure metric |

ROC | Receiver operating characteristic curve |

AUC values | Area under the ROC curve |

MMAP | Monthly maritime accident prediction model |

YMAP | Yearly maritime accident prediction model |

M-YMAP | Multi-yearly maritime accident prediction model |

VTS | Vessel Traffic Service |

pyais PyPI | Python Pyais package |

MSA | Maritime Safety Administration |

$i$ | Influencing factor $i$ |

$j$ | Influencing factor$j$ |

${r}_{ij}$ | The correlation coefficient between factor $i$ and factor $j$ |

$\overline{{x}_{i}}$ | The mean value of factor $i$ |

$\overline{{x}_{j}}$ | The mean value of factor $j$ |

${\sigma}_{i}$ | The sample standard deviations of factor $i$ |

${\sigma}_{j}$ | The sample standard deviations of factor $j$ |

$m$ | The number of samples |

$k$ | The $kth$ sample, k = 1, 2, 3, …, $m$. |

TP | True positive |

TN | True negative |

FP | False positive |

FN | False negative |

H-class | Very high–high-susceptibility class |

cbr | Cost–benefit ratio |

$s$ | Susceptibility class s |

${cbr}^{s}$ | Cost–benefit ratio of susceptibility class s |

${acc}_{n}^{s}$ | The number of accidents in class s |

$no{-acc}_{n}^{s}$ | The number of non-accident grids in class s |

Acc | Accident |

F1–F12 | The abbreviations for influencing factors, which can be found in Table 1 |

**Table A2.**The frequencies of different accident types (except Others) in each month between 2015 and 2021.

Month | Others | Collision | Sank | Stranding | Fire and Explosion | Touch Rocks | Wind | Touch | Damage by Waves | Operational Pollution |
---|---|---|---|---|---|---|---|---|---|---|

1 | 108 | 48 | 38 | 38 | 21 | 11 | 3 | 4 | 2 | 1 |

2 | 83 | 27 | 28 | 31 | 22 | 10 | 0 | 3 | 1 | 0 |

3 | 98 | 69 | 34 | 46 | 24 | 16 | 1 | 10 | 0 | 2 |

4 | 108 | 66 | 41 | 35 | 10 | 16 | 7 | 8 | 0 | 1 |

5 | 98 | 38 | 32 | 26 | 18 | 7 | 3 | 2 | 1 | 1 |

6 | 105 | 24 | 28 | 35 | 12 | 7 | 1 | 3 | 2 | 2 |

7 | 125 | 25 | 43 | 44 | 18 | 16 | 21 | 4 | 9 | 2 |

8 | 161 | 66 | 37 | 42 | 27 | 23 | 32 | 5 | 3 | 2 |

9 | 126 | 70 | 42 | 51 | 28 | 25 | 9 | 4 | 5 | 0 |

10 | 150 | 61 | 56 | 50 | 24 | 23 | 15 | 3 | 3 | 2 |

11 | 100 | 53 | 44 | 30 | 20 | 11 | 2 | 3 | 4 | 1 |

12 | 144 | 50 | 51 | 31 | 11 | 23 | 3 | 5 | 3 | 1 |

**Figure A1.**The frequencies of different accident types (except Others) each month between 2015 and 2021.

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**Figure 1.**Study area. Map lines delineate study areas and do not necessarily depict accepted national boundaries.

**Figure 2.**The frequency of accidents for (

**a**) each severity level, (

**b**) each month, (

**c**) each ship type, and (

**d**) each accident type, between 2015 and 2021.

**Figure 3.**Boxes of the influencing factors from 2016 to 2020. The boxes show the interquartile ranges, red lines and blue lines indicate the median and mean values, and the whiskers extend to the minimum/maximum values within the 1.5 interquartile range of the lower/upper quartiles.

**Figure 4.**The Pearson correlation coefficient matrix of the influencing factors in (

**a**) MMAP, (

**b**) YMAP, and (

**c**) M-YMAP models. Each square corresponds to the correlation between two factors. × indicates that the correlation coefficient is “not significant” (p-value < 0.05).

**Figure 5.**Performance of the random forest model: (

**a**) accuracy (A), recall (R), precision (P), and F1-measure (F1-m) and (

**b**) ROC curve.

**Figure 6.**Maritime susceptibility maps and observed accidents: (

**a**–

**c**) January 2017, August 2017, and August 2020 susceptibility maps generated using MMAP model and associated observed accidents; (

**d**,

**e**) 2017 and 2020 susceptibility maps generated using YMAP model and associated observed accidents; (

**f**) the percentage of observed accidents at different susceptibility levels from 2015 to 2020; (

**g**,

**h**) 2016–2020 maritime susceptibility maps generated using the M-YMAP model and associated observed accidents. The rings show the percentages of accidents at different susceptibility levels.

**Figure 7.**Performance of the random forest model for blind data: (

**a**) accuracy (A), recall (R), precision (P), and F1-measure (F1-m) and (

**b**) ROC curve.

**Figure 8.**Maritime susceptibility maps developed using the MMAP model and observed accidents for (

**a**) January 2021, (

**b**) February 2021, (

**c**) March 2021, and (

**d**) April 2021.

**Figure 9.**The relative importance of influencing factors in the (

**a**) MMAP, (

**b**) YMAP, and (

**c**) M-YMAP models using RF.

**Figure 10.**The model result change as the ratio of non-accident and accident samples in the MMAP model in January 2021: (

**a**) the evaluation metrics; (

**b**) the different susceptibility class area percentages of the study area; (

**c**) the percentages of the observed accidents in different susceptibility classes; and (

**d**) cbrs.

No. | Data | Resolution (Original) | Unit | Description | Source |
---|---|---|---|---|---|

F1 | ShipDensity | - | pc | The number of ships in a unit | https://www.msa.gov.cn/ (accessed on 20 March 2023) |

F2 | AveLength | - | m | The average length in a unit | https://www.msa.gov.cn/ (accessed on 20 March 2023) |

F3 | AveWidth | - | m | The average width in a unit | https://www.msa.gov.cn/ (accessed on 20 March 2023) |

F4 | FishRatio | - | ratio | The ratio of fishing vessels in a unit | https://www.msa.gov.cn/ (accessed on 20 March 2023) |

F5 | Bathymetry | 1′ (~2 km) | m | The bathymetry of the grid | https://www.ngdc.noaa.gov/mgg/global/etopo5.HTML (accessed on 23 March 2023) |

F6 | DisShore | - | km | The distance from shore | https://gadm.org/download_world.html (accessed on 24 March 2023) |

F7 | MaxTemp_Days | 0.25° (~27 km) | °C | The number of days of temperatures exceeding 35 °C | https://cds.climate.copernicus.eu/portfolio/dataset/reanalysis-era5-single-levels (accessed on 28 March 2023) |

F8 | MinTemp_Days | 0.25° (~27 km) | °C | The number of days of temperatures lower than 0 °C | https://cds.climate.copernicus.eu/portfolio/dataset/reanalysis-era5-single-levels (accessed on 28 March 2023) |

F9 | MaxPre_Days | 0.25° (~27 km) | mm | The number of days of precipitation exceeding 50 mm | https://cds.climate.copernicus.eu/portfolio/dataset/reanalysis-era5-single-levels (accessed on 28 March 2023) |

F10 | MaxWind_Days | 0.25° (~27 km) | m/s | The number of days of the wind speed exceeding 17.2 m/s | |

F11 | Maxcloud_Days | 0.25° (~27 km) | ratio | The number of days of the cloud height exceeding 0.8 | |

F12 | MaxWave_Days | 0.25° (~27 km) | m | The number of days of the wave height exceeding 2.5 m |

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## Share and Cite

**MDPI and ACS Style**

Zhu, W.; Wang, S.; Liu, S.; Yang, L.; Zheng, X.; Li, B.; Zhang, L.
Dynamic Multi-Period Maritime Accident Susceptibility Assessment Based on AIS Data and Random Forest Model. *J. Mar. Sci. Eng.* **2023**, *11*, 1935.
https://doi.org/10.3390/jmse11101935

**AMA Style**

Zhu W, Wang S, Liu S, Yang L, Zheng X, Li B, Zhang L.
Dynamic Multi-Period Maritime Accident Susceptibility Assessment Based on AIS Data and Random Forest Model. *Journal of Marine Science and Engineering*. 2023; 11(10):1935.
https://doi.org/10.3390/jmse11101935

**Chicago/Turabian Style**

Zhu, Weihua, Shoudong Wang, Shengli Liu, Libo Yang, Xinrui Zheng, Bohao Li, and Lixiao Zhang.
2023. "Dynamic Multi-Period Maritime Accident Susceptibility Assessment Based on AIS Data and Random Forest Model" *Journal of Marine Science and Engineering* 11, no. 10: 1935.
https://doi.org/10.3390/jmse11101935