船舶大气污染物排放清单缺失信息处理方法

    Method for supplementing missing information in ship air pollutant emission inventory

    • 摘要: 基于船舶自动识别系统(AIS)数据编制船舶排放清单是国际主流方法,在我国针对该方法的研究处于跟进阶段。AIS动力法的关键是船舶的静态信息,但实际情况是船舶注册登记静态数据信息缺失严重,严重影响了AIS动力法编制船舶大气排放清单的准确性。特别是我国船舶中小船众多,已有清单缺失信息补充方法不符合我国国情。为此,本文构建了基于大数据分析的补充船舶静态信息缺失数据的模型,结果显示:与数理统计、统一赋值等已有方法相比,本文构建的基于大数据分析法的偏差平均值更小、标准差也更小,更接近船舶静态信息的实际值,可以有效解决船舶静态信息缺失问题。

       

      Abstract: The ship emission inventory based on automatic identification system(AIS)is a mainstream international method,and research in our country is at the follow-up stage. The key to the AIS dynamic method is the static information of the ship,but there is a severe lack of static data information in the ship registration database,which seriously affects the accuracy of the emission inventories. In our country,there are many small ships,and the existing missing information supplement methods do not conform to the national conditions of our country. Therefore,this paper constructs a model to supplement the missing static information of ships based on big data analysis. The results show that compared with existing methods such as mathematical statistics and valuation,the big data analysis method constructed in the paper has a smaller average deviation and a smaller standard deviation,which is closer to the actual value of the ship’s static information,and can effectively solve the problem of missing static information of ships.

       

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