An Air Quality Modeling and Disability-Adjusted Life Years (DALY) Risk Assessment Case Study: Comparing Statistical and Machine Learning Approaches for PM_2.5 Forecasting

Keyphrases

• Air Pollutant Concentrations 33%
• Air Pollution Characteristics 33%
• Air Pollution Control 33%
• Air Pollution Modelling 33%
• Air Quality 33%
• Air Quality Modeling 100%
• Ambient Air Pollutants 33%
• Astana 66%
• Carbon Monoxide Concentration 33%
• Central Asia 33%
• Climate Characteristics 33%
• Disability-adjusted Life Years 100%
• Graves 33%
• Harsh Climate 33%
• Healthcare Professionals 33%
• Human Exposure 33%
• Kazakhstan 33%
• Machine Learning Approach 100%
• Meteorological Parameters 66%
• Multiple Linear Regression 66%
• Multiple Linear Regression Model 33%
• Non-heating Period 33%
• Northern Asia 33%
• PM10 33%
• PM2.5 33%
• PM2.5 Concentration 33%
• PM2.5 Prediction 100%
• Politicians 33%
• Pollution Prevention 33%
• Predictive Variables 33%
• Predictor Variables 33%
• Quality Issues 33%
• Random Forest 33%
• Random Forest Algorithm 33%
• Random Forest Model 33%
• Risk Assessment 100%
• Statistical Learning 100%

INIS

• accuracy 20%
• air pollution 100%
• air quality 100%
• algorithms 20%
• asia 20%
• carbon monoxide 20%
• cities 20%
• climates 40%
• concentration 60%
• forecasting 100%
• forests 60%
• heating 40%
• humans 20%
• kazakhstan 20%
• machine learning 100%
• meteorology 40%
• mitigation 20%
• modeling 100%
• output 20%
• populations 20%
• prediction 40%
• randomness 60%
• risk assessment 100%

Earth and Planetary Sciences

• Air Pollutant 50%
• Air Pollution 50%
• Air Pollution Modeling 25%
• Air Quality 25%
• Air Quality Modeling 100%
• Ambient Air 25%
• Asia 25%
• Carbon Monoxide 25%
• Kazakhstan 25%
• Machine Learning 100%
• Meteorological Parameter 50%
• Multiple Use 25%
• Particular Matter 2.5 100%
• Pollutant Concentration 25%