Lightweight visibility prediction method based on machine learning

Maochan Zhen; Mingjian Yi; Tao Luo; Feifei Wang; Kaixuan Yang; Xuebin Li

doi:10.1117/12.2681275

23 May 2023 Lightweight visibility prediction method based on machine learning

Maochan Zhen, Mingjian Yi, Tao Luo, Feifei Wang, Kaixuan Yang, Xuebin Li

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126452Y (2023) https://doi.org/10.1117/12.2681275
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

Since there are many possible influencing factors of visibility, lightweight data requirements in practical applications of machine learning in visibility prediction can reduce the corresponding data observation cost and collection difficulty. By using the long-term measured data in Qingdao, this research comprehensively compares the performance of five common machine learning methods under different training parameter schemes, including XGBoost, LightGBM, Random Forest (RF), Support Vector Machine (SVM) and Multiple Linear Regression (MLR). The lightweight visibility prediction schemes based on pollutant parameter optimization are established. The seasonal training data of five machine learning models is preprocessed, and then performance evaluations of predictions are carried out. The analysis results show that in terms of models, ensemble learning models, including XGBoost, LightGBM, and RF, have significantly better seasonal visibility prediction effects than SVM and MLR models; XGBoost and LightGBM also have slightly better prediction effects than RF models. In terms of pollutant parameters, solid pollutants have a greater impact on visibility prediction than gaseous pollutants; PM2.5 is more influential than PM10 in visibility prediction. The visibility prediction scheme with six parameters using meteorological parameters and PM2.5 based on XGBoost or LightGBM model is preferably established in this research. This scheme can achieve the same prediction performance as the 11 parameter prediction scheme. The Correlation Coefficient (CC) of the results is around 0.85. The results of this study can not only be used to provide a machine learning scheme reference for practical visibility prediction applications, but also help to deepen the understanding of the factors affecting visibility.

Citation Download Citation

Maochan Zhen, Mingjian Yi, Tao Luo, Feifei Wang, Kaixuan Yang, and Xuebin Li "Lightweight visibility prediction method based on machine learning", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126452Y (23 May 2023); https://doi.org/10.1117/12.2681275

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Visibility

Machine learning

Education and training

Atmospheric modeling

Performance modeling

Meteorology

Data modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years