Spatial air quality index prediction model based on decomposition, adaptive boosting, and three-stage feature selection: A case study in China

2020 ◽  
Vol 265 ◽  
pp. 121777 ◽  
Author(s):  
Hui Liu ◽  
Chao Chen
Keyword(s):  
Feature Selection ◽  
Air Quality ◽  
Prediction Model ◽  
Quality Index ◽  
Air Quality Index ◽  
Adaptive Boosting ◽  
Model Based

Inferring air pollution from air quality index by different geographical areas: case study in India

2019 ◽  
Vol 12 (11) ◽  
pp. 1347-1357 ◽  
Author(s):  
Rohit Sharma ◽  
Raghvendra Kumar ◽  
Devendra Kumar Sharma ◽  
Le Hoang Son ◽  
Ishaani Priyadarshini ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index

A combined model based on feature selection and support vector machine for PM2.5 prediction

2021 ◽  
pp. 1-15
Author(s):  
Xiaocong Lai ◽  
Hua Li ◽  
Ying Pan
Keyword(s):  
Air Pollution ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Air Quality ◽  
Prediction Model ◽  
Meteorological Factors ◽  
Feature Selection Method ◽  
Support Vector ◽  
Combined Model ◽  
Model Based

With the increasing attention to the environment and air quality, PM2.5 has been paid more and more attention. It is expected to excavate useful information in meteorological data to predict air pollution, however, the air quality is greatly affected by meteorological factors, and how to establish an effective air quality prediction model has always been a problem that people urgently need to solve. This paper proposed a combined model based on feature selection and Support Vector Machine (SVM) for PM2.5 prediction. Firstly, aiming at the influence of meteorological factors on PM2.5, a feature selection method based on linear causality is proposed to find out the causality between features and select the features with strong causality, so as to remove the redundant features in air pollution data and reduce the workload of data analysis. Then, a method based on SVM is proposed to analyze and solve the nonlinear problems in the data, for reducing the prediction error, a method of particle swarm optimization is also used to optimize SVM parameters. Finally, the above methods are combined into a prediction model, which is suitable for the current air pollution control. 12 representative data sets on the UCI (University of California, Irvine) website are used to verify the combined model, and the experimental results show that the model is feasible and effective.

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