Ocean big data and applications in ship behavior prediction under disaster weather

Donghai WANG; Feng LU; Xiaorong FANG; Gang GUO

doi:10.11959/j.issn.2096-0271.2017044

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Ocean big data and applications in ship behavior prediction under disaster weather

APPLICATION | 更新时间：2024-06-03

- Ocean big data and applications in ship behavior prediction under disaster weather
- Big data research Vol. 3, Issue 4, Pages: 81-90(2017)
- 作者机构：
- 作者简介：
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- DOI：10.11959/j.issn.2096-0271.2017044
  CLC： TP181
- Online First：2017-07，
  
  Published：20 July 2017
- 稿件说明：
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Donghai WANG, Feng LU, Xiaorong FANG, et al. Ocean big data and applications in ship behavior prediction under disaster weather[J]. Big data research, 2017, 3(4): 81-90.
DOI：

Donghai WANG, Feng LU, Xiaorong FANG, et al. Ocean big data and applications in ship behavior prediction under disaster weather[J]. Big data research, 2017, 3(4): 81-90. DOI： 10.11959/j.issn.2096-0271.2017044.

摘要

随着海洋数据量的爆炸式增长，海洋大数据受到越来越多的关注。主要分析和总结了当前海洋大数据的研究现状和关键技术，聚焦了机器学习在海洋大数据中的模型预测研究的实例，对海上船舶在灾害天气（台风）下的行为进行了回归训练和预测。通过构建和对比决策树、Bagging、随机森林等多种机器学习算法，对样本数据进行学习、预测和检验评估。最终结果表明，随机森林方法在灾害天气下船舶密度的预测应用中具有良好和稳健的效果。

Abstract

With the explosive growth of marine data

the ocean big data have received more attention and concern recently.The current status and key technologies of ocean big data here were summarized and analyzed.A specific case about the application of machine learning in the prediction model of ocean big data was also focused

which was a forecasting test of maritime ships behavior based on regression training in disaster weather (typhoon).The sample data for validating and evaluating three machine learning algorithms of decision tree

Bagging and random forest were trained and tested.The final results prove the best and robust effect of the random forest algorithm in the prediction of ship density under the disaster weather.

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references

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