Application of a Fusion Model Based on Machine Learning in Visibility Prediction

To improve the accuracy of atmospheric visibility (V) prediction based on machine learning in different pollution scenarios, a new atmospheric visibility prediction method based on the stacking fusion model (VSFM) is established in this paper. The new method uses the stacking strategy to fuse two ba...

Full description

Saved in:
Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-03, Vol.15 (5), p.1450
Main Authors: Zhen, Maochan, Yi, Mingjian, Luo, Tao, Wang, Feifei, Yang, Kaixuan, Ma, Xuebin, Cui, Shengcheng, Li, Xuebin
Format: Article
Language:English
Subjects:
Accuracy
Aerosols
Algorithms
Altitude
Atmospheric models
Feature selection
fusion model
Humidity
Image processing
Learning algorithms
LightGBM
Machine learning
Meteorological data
Methods
Optics
Pollutants
Pollution
Pollution sources
Predictions
Remote sensing
Stacking
Visibility
visibility prediction
XGBoost
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To improve the accuracy of atmospheric visibility (V) prediction based on machine learning in different pollution scenarios, a new atmospheric visibility prediction method based on the stacking fusion model (VSFM) is established in this paper. The new method uses the stacking strategy to fuse two base learners—eXtreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM)—to optimize prediction accuracy. Furthermore, seasonal feature importance evaluations and feature selection were utilized to optimize prediction accuracy in different seasons with different pollution sources. The new VSFM was applied to 1-year environmental and meteorological data measured in Qingdao, China. Compared to other traditional non-stacking models, the new VSFM improved precision during different seasons, especially in extremely low-visibility scenarios (V< 2 km). The TS score of the VSFM was significantly better than that of other models. For extremely low-visibility scenarios, the VSFM had a threat score (TS) of 0.5, while the best performance of other models was less than 0.27. The new method is promising for atmospheric visibility prediction under complex urban pollution conditions. The research results can also improve our understanding of the factors that influence urban visibility.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15051450