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

被引:3
作者
Zhu, Weihua [1 ,2 ,3 ]
Wang, Shoudong [1 ,2 ]
Liu, Shengli [1 ,2 ]
Yang, Libo [1 ,2 ]
Zheng, Xinrui [1 ,2 ]
Li, Bohao [4 ]
Zhang, Lixiao [3 ]
机构
[1] Minist Transport, Transport Planning & Res Inst, Beijing 100028, Peoples R China
[2] Lab Transport Safety & Emergency Technol, Beijing 100028, Peoples R China
[3] Beijing Normal Univ, Sch Environm, State Key Joint Lab Environm Simulat & Pollut Cont, Beijing 100875, Peoples R China
[4] Beijing Normal Univ, Sch Natl Safety & Emergency Management, Beijing 100875, Peoples R China
关键词
dynamic maritime accident susceptibility; random forest; AIS data;
D O I
10.3390/jmse11101935
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Maritime accidents, such as ship collisions and oil spills, directly affect maritime transportation, pollute the water environment, and indirectly threaten life and property safety. Predicting the maritime accident susceptibility and taking measures in advance can effectively avoid the accident probability and reduce the risk. Therefore, this study established dynamic multi-period (monthly, yearly, and five-yearly) maritime accident prediction models based on the random forest (RF) algorithm and Automatic Identification System (AIS) data for susceptibility assessment. First, based on historical maritime accidents and influencing factor data, we generated the feature matrixes and selected the conditioning factors using the Pearson correlation coefficient. Then, we constructed the accident susceptibility models using the RF method and evaluated the model performances based on the accuracy, recall, precision, F1-measure, ROC, and AUC values. Finally, we developed accident susceptibility maps for different period scales. The results show that the monthly, yearly, and five-yearly models performed well according to the validation values. And the three-period susceptibility maps show similar patterns. The high-susceptibility areas are close to the shore, especially from the Shanghai shore to the Guangxi shore. In addition, the ship density and bathymetry are the most critical factors among the ten influencing factors in the three models, contributing around 25% and 20% of the total information. These models and maps can provide technological support for maritime accident susceptibility assessment on a multi-period scale, which can be helpful for route planning and resource allocation in marine management.
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页数:19
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