主办单位:中国气象局沈阳大气环境研究所
国际刊号:ISSN 1673-503X
国内刊号:CN 21-1531/P

Journal of Meteorology and Environment ›› 2021, Vol. 37 ›› Issue (3): 132-138.doi: 10.3969/j.issn.1673-503X.2021.03.018

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Navigation risk assessment of Guangzhou port based on Monte Carlo method

Jing ZHANG1(),Zhi-jian ZHANG2,Xin-yu ZHOU1,*()   

  1. 1. Guangzhou Meteorological Observatory, Guangzhou 511430, China
    2. Guangzhou Emergency Early Warning Release Center, Guangzhou 511430, China
  • Received:2020-11-11 Online:2021-06-30 Published:1900-01-01
  • Contact: Xin-yu ZHOU E-mail:469578282@qq.com;xiao-yu1985114@163.com

Abstract:

To obtain the characteristics of navigation risk of Guangzhou port at different visibility levels, the qualitative navigation risks of Guangzhou port under different visibility were quantified, to enhance the risk control ability of navigation accidents and improve the resources utilization rate of Guangzhou port.By analyzing the frequency of ship accidents and the severity of the consequences under different visibility, the Monte Carlo simulation of the accident data under three levels of visibility was carried out after the probability statistics of the two, which effectively increased the navigation accidents samples of Guangzhou port.The simulation results under three levels of visibility were obtained, and then the risk distribution characteristics of visibility within 0 and 12 km were acquired.The results show that the probability distribution model based on the Monte Carlo simulation method can effectively solve the problem of small samples of navigation risk, and the risk result is reliable.The risk is lowest at poor visibility (Vis ≤ 5 km), followed by good visibility (Vis ≥ 10 km).When it is restricted (5 km <Vis < 10 km), the risk is approximately 1.7-2.4 times of the other two cases.It can be seen that this method can well assess the navigation risk of Guangzhou port with visibility within 0-12 km, and provide a reference for delineating risk level standards.

Key words: Visibility, Monte Carlo, Risk assessment, Probability distribution

CLC Number: