基于SVM-BP神经网络的气象能见度数据缺值预估
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1.南京信息工程大学自动化学院;2.南京信息工程大学;3.安徽省气象信息中心

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国家自然科学基金 61573190,61571014 安徽省气象局科研项目:KM201907 基于机器学习的能见度质量控制方法研究


SVM - BP Neural Network Based Meteorological Visibility Data Filling
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Affiliation:

1.School of Automation, Nanjing University of Information Science & Technology;2.Nanjing University of Science & Information Technology;3.Anhui Meteorological Information Center

Fund Project:

1 CICAEET, School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China; 2 Anhui Meteorological Information Center, Hefei 230031, china

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    摘要:

    自动气象站能见度检测仪多采用光学装置采样,一些雨雪、粉尘等天气因素对部分仪器镜头造成污染,导致能见度要素数据缺测。针对能见度数据缺失问题,本文选用部分安徽气象站的历年数据,首先运用灰色关联分析方法筛选出与能见度密切相关的其他气象要素,通过支持向量机和BP 神经网络单一预估方法预估不同地形的能见度缺失值,然后采用最优权重组合将两种方法预估的能见度值进行组合,并与单一预估方法进行对比。结果表明组合预估方法的结果误差均值小,整体准确度高,可以保证台站观测资料的完备性,为短时天气预报、实况分析和气象公共服务工作提供有效依据。

    Abstract:

    Most automatic weather station use optical devices to sample visibility. There are two problems with this. One problem is inaccurate sample date caused by weather factors such as rain, snow or dust in which the lens of those optimal devices might be soiled, and thus the collected data is not accurate. Another problem is data missing due to aging equipment, equipment maintenance, etc. To get rid of those wrong sample and provide complete data for meteorological prediction, this paper proposes a SVM-BP neural network based data quality control method for weather visibility according to those historical data from the Anhui Meteorological Bureau. Firstly, the grey correlation analysis method is used to screen out other meteorological elements closely related to visibility. Secondly, the optimal weight combination is used to combine the support vector machine and the BP neural network. The combined estimation of the visibility of different terrains is then carried out and compared with a single estimation method. The result shows that the combined estimation method has a smaller mean error, higher overall accuracy, and provide an effective theoretical basis for short-term weather forecasting, live analysis, scientific research and public service.

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刘霄瑜,殷利平,温华洋,邱康俊.基于SVM-BP神经网络的气象能见度数据缺值预估[J].南京信息工程大学学报,,():

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  • 收稿日期:2020-10-12
  • 最后修改日期:2021-03-01
  • 录用日期:2022-12-07
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