舰船电子工程2023，Vol.43Issue(12)：182-186,237,6.DOI:10.3969/j.issn.1672-9730.2023.12.037
基于机器学习对机场能见度预测模型研究
Research on Airport Visibility Prediction Model Based on Machine Learning
摘要
Abstract
In order to further accurately predict the visibility change trend of Maotai Airport,this paper first conducts statisti-cal analysis on the visibility change trend of Maotai Airport in 2018 and Pearson correlation analysis on the observed meteorological elements based on the annual airport meteorological office data in 2018.It is found that the relative humidity is significantly positive-ly correlated with the airport visibility change,and the temperature dew point difference is significantly negatively correlated with the airport visibility.Then,the visibility of Maotai Airport is predicted by multiple linear regression(MLR),BP neural network and radi-al basis function(BBF)neural network respectively.By comparing the goodness of fit,average error,mean square error and root mean square error,it is found that RBF neural network model performs better in error control and prediction accuracy than MLR and BP neural networks.Therefore,it can be selected as the visibility prediction model of Maotai Airport,which is of great significance to the safe operation of Maotai Airport.关键词
RBF神经网络/多元线性回归/BP神经网络/机场能见度预测Key words
RBF neural network/multiple linear regression/BP neural network/airport visibility prediction分类
航空航天引用本文复制引用
袁敏,李忠堃,洪震宇,贾志杰,吴戈..基于机器学习对机场能见度预测模型研究[J].舰船电子工程,2023,43(12):182-186,237,6.基金项目
国家重点研发计划"交通基础设施"重点专项2021年"揭榜挂帅"榜单项目(编号:2021YFB2601701-01)资助. （编号:2021YFB2601701-01）
