Air Quality Index and Air Pollutant Concentration Prediction Based on Machine Learning Algorithms
Problem
Air pollution forecasts are needed to warn people and control pollution; accurate prediction of AQI and pollutant concentrations was addressed.
Why it matters
Air pollution is an important environmental issue and forecasts support public warning and control.
What the AI did
Built support vector regression (SVR) and random forest regression (RFR) models on two public datasets to predict Beijing's AQI and NOX concentration in an Italian city.
Results
SVR best for AQI: RMSE 7.666, R2 0.9776, r 0.9887; RFR best for NOX: RMSE 83.6716, R2 0.8401, r 0.9180.
Impact
Demonstrates machine learning as an efficient, convenient approach to air quality prediction.
Record
| Venue | Applied Sciences |
|---|---|
| Published | 2019 |
| Work type | article |
| Authors | Huixiang Liu, Qing X. Li, Dongbing Yu, Yu Gu |
| Citations | 226 (OpenAlex, at import time) |
| References | 27 |
| Model or method | SVR and RFR |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/app9194069 |
| Open access | gold |
| Keywords | air quality index, SVR, random forest, prediction, air pollution |
Importance rating
real but narrower advance
Air pollution is called an important environmental issue and forecasts support public warning/control, but the abstract gives no quantified scale of impact.
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