渔业现代化

渔业现代化杂志

• 论文 • 上一篇    下一篇

BP神经网络在渔船航行安全预警中的应用

  

  1. （城市环境过程与数字模拟国家重点实验室培育基地，资源环境与地理信息系统北京市重点实验室，
    首都师范大学资源环境与旅游学院，北京 100048）
  • 出版日期:2016-02-20 发布日期:2022-09-01
  • 通讯作者: 孙永华（1982—），男，讲师，博士，研究方向：高光谱遥感、城市遥感、GIS软件工程。E-mail：syhua1982@163.com
  • 作者简介:王金浩（1990—），男，硕士研究生，研究方向：海洋地理信息系统。E-mail：wjhao1990@126.com
  • 基金资助:
    国家海洋公益性项目“海洋渔业安全环境保障服务系统关键技术研究及示范应用(201205006)”和“海洋预报业务化系统模块化构建与应用示范(201205017)”

Application of BP neural network in the early warning of fishing vessel navigation safety

  1. （State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation,
    Beijing Key Laboratory of Resource Environment and Geographic Information System,
    College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China）
  • Online:2016-02-20 Published:2022-09-01

PDF (PC)

摘要： 渔船在海上航行时由于船体自身结构或者海面风浪等因素的影响处于潜在的威胁当中。为了研究渔船在海洋环境中可能会遭受的风险，采用基于BP神经网络算法，对渔船吨位、发动机功率、渔船材质、渔船船龄以及渔船所处海面风等级、海面浪等级等6个预警指标要素构成的渔船预警模型进行评估，最终确定渔船在海上航行时的风险等级。在构建风险预警模型中使用了400件渔船事故案例，将训练样本按照数量划分为多个级别进行验证。预警结果与实际结果的计算结果显示，模型的正确率为79.76%~83.62%，其中在训练样本数为测试样本数的0.75倍时，模型精度最高。研究表明，基于BP神经网络的渔船风险预警模型，其评估结果与渔船实际事故状态基本相符。该模型的建立为渔船海上航行提供了安全保障。

关键词: 渔船航行, 安全预警, 预警指标, BP神经网络

Abstract: During the voyage, the fishing vessel is in a potential threat because of its own structure or the influence of sea surface wind and waves. In order to study the risk of fishing vessels in the marine environment, based on the BP neural network algorithm, the fishing boats early warning model which is composed of 6 early warning indicators : fishing vessel tonnage, engine power, material, fishing vessels age, sea breeze level, wave level, were evaluated and then the sea operations risk level for fishing vessels were finally determined. 400 fishing vessel accident cases were selected to develop the risk early warning model  and  the model was verified through classification of multiple levels for the training samples. The results of early warning and the actual results of statistical calculation showed, the correct rate remained at 79.76%-83.62%, in which when the training sample number was 0.75 times as the number of test samples, the accuracy of the model is highest. In conclusion, the assessment results of fishing vessel risk early warning model based on BP neural network was basically consistent with the actual condition of accident, which could provide guarantee for safe navigation .

Key words: fishing vessel navigation, safty warning, warning indicator, BP neural network