Visibility Prediction Based on Machine Learning Algorithms

被引：13

作者：

Zhang, Yu ^{[1
]}

Wang, Yangjun ^{[2
]}

Zhu, Yinqian ^{[3
]}

Yang, Lizhi ^{[4
]}

Ge, Lin ^{[1
]}

Luo, Chun ^{[1
]}

机构：

[1] China Airforce Qionglai Airport, 95746 Troops, Chengdu 610000, Peoples R China

[2] Natl Univ Def Technol, Inst Meteorol & Oceanog, Changsha 410000, Peoples R China

[3] Runzhou Dist Comm Communist Youth League Zhenjian, Zhenjiang 212000, Jiangsu, Peoples R China

[4] 78127 Troops, Chengdu 610000, Peoples R China

来源：

ATMOSPHERE | 2022年 / 13卷 / 07期

关键词：

visibility; machine learning; PCA; FOG;

D O I：

10.3390/atmos13071125

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

In this study, ground observation data were selected from January 2016 to January 2020. First, six machine learning methods were used to predict visibility. We verified the accuracy of the method with and without principal components analysis (PCA) by combining actual examples with the European Centre for Medium-Range Weather Forecast (ECMWF) data and National Centers for Environmental Prediction (NECP) data. The results show that PCA can improve visibility prediction. Neural networks have high accuracy in machine learning algorithms. The initial visibility data plays an important role in the visibility forecast and can effectively improve forecast accuracy.

引用

页数：12

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