Intelligent Forecasting of Air Quality and Pollution Prediction Using Machine Learning
Problem
Predicting PM2.5 pollutant concentrations in polluted cities to support air quality monitoring.
Why it matters
PM2.5 exposure harms public health, so reliable forecasts can support pollution warnings and planning.
What the AI did
Trained and compared multiple machine learning models (linear regression, random forest, KNN, ridge/lasso, XGBoost, AdaBoost) to predict PM2.5, evaluated via MAE, MAPE and RMSE.
Results
XGBoost: MAE 8.27, MAPE 0.40, RMSE 13.85; AdaBoost: 9.23, 0.45, 10.59; random forest: 39.84, 1.94, 54.59; KNN: 49.13, 2.40, 69.92
Impact
Identified XGBoost and AdaBoost as the most reliable PM2.5 prediction models among those tested.
Record
| Venue | Adsorption Science & Technology |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | D. Kothandaraman, N. Praveena, K. Varadarajkumar, B. Madhav Rao, Dharmesh Dhabliya, Shivaprasad Satla, Worku Abera |
| Citations | 81 (OpenAlex, at import time) |
| References | 26 |
| Model or method | XGBoost, AdaBoost, random forest, KNN (best performers) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1155/2022/5086622 |
| Open access | gold |
| Keywords | PM2.5, air quality, XGBoost, machine learning, forecasting, pollution |
Importance rating
significant — thousands to millions affected
PM2.5 exposure harms public health in polluted cities; forecasts support warnings and planning, but abstract gives no quantified scale.
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