Predicting fine particulate matter (PM2.5) in the greater london area: An ensemble approach using machine learning methods

Keyphrases

• Random Forest 100%
• Machine Learning Techniques 100%
• Ensemble Approach 100%
• K-nearest 100%
• PM2.5 Level 100%
• Gradient Boosting Machine 100%
• Fine Particulate Matter PM2.5 100%
• Greater London 100%
• Machine Learner 100%
• Health Studies 50%
• Prediction Model 50%
• Air Pollution Exposure 50%
• Ensemble Model 50%
• Three-machine 50%
• Environmental Health 50%
• Technological Progress 50%
• Machine Learning Approach 50%
• Pollution 50%
• Adept 50%
• Aerosol Optical Depth 50%
• Meteorological Data 50%
• Pollutant Levels 50%
• Geographic Range 50%
• Health Researchers 50%
• Ensemble Machine Learning 50%
• Spatial Variability 50%
• Novel Machine 50%
• Land Use Data 50%
• Exposure Model 50%
• Model Making 50%

Earth and Planetary Sciences

• London 100%
• Machine Learning 100%
• Particular Matter 2.5 100%
• Fine Particulate Matter 100%
• Air Pollution 33%
• Land Use 33%
• Environmental Health 33%
• Spatial Variation 33%
• Pollutant Level 33%