基于主成分分析与BP神经网络的雾天能见度等级预报
Forecasting Model for Visibility Levels in Foggy Weathers Based on Principal Components Analysis and BP Neural Network
本文利用江苏省昆山市2012-2014年逐时常规气象观测数据、空气质量监测数据和能见度数据，分析了雾天能见度与各要素的相关性，并通过主成分分析提取影响主成分，建立了基于主成分的三层BP神经网络模型。结果表明，雾天能见度不仅与气象要素呈现较好的相关性，空气污染物（如NO2、O3、PM10）对雾天能见度也有较大影响；主成分神经网络模型能够较准确地预测雾天能见度等级（大雾、浓雾、(特)强浓雾），对提高雾天能见度精细化预报效果具有良好的参考价值。
Based on the hourly weather data, the environmental atmosphere quality monitoring data and visibility data from 2012 to 2014 in the Kunshan city of Jiangsu Province, the correlation coefficient between visibility and different variables was analyzed and a three-layer BP neural network model was constructed based on the principal components. The results show that visibility in the foggy weathers not only has a good correlation to meteorological factors but also is influenced by air pollutants such as NO2, O3 and PM10; the model can accurately predict the visibility levels(heavy fog, dense fog, extremely dense fog), and has a good reference value to improve the ability of fine fog forecast.
包云轩、黄政
大气科学（气象学）环境污染、环境污染防治
应用气象雾能见度等级主成分分析神经网络
pplied MeteorologyFogVisibility levelPrincipal component analysisNeural Network
包云轩,黄政.基于主成分分析与BP神经网络的雾天能见度等级预报[EB/OL].(2016-04-26)[2025-08-10].http://www.paper.edu.cn/releasepaper/content/201604-333.点此复制
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