Please use this identifier to cite or link to this item:
|Title:||Prediction of Air Pollution Concentration Using Weather Data and Regression Models|
|Abstract:||Air pollution is becoming a global environmental problem, in both developed and developing countries. It has greatly impacted the health and lives of millions of people, thus increasing mortality rates and pollution induced diseases reports. This paper proposes machine learning methods for predicting the rates of possibly increased air pollution in several areas, by processing the gathered data from multiple weather and air quality meter stations. The data has been gathered over a period of several years including air quality and pollution data and weather data including temperature, humidity and wind characteristics. The development process included feature extraction, feature selection for removing redundancy, and finally training multiple regression models and hyperparameter optimization. Pollutants and air quality index (AQI) were used as target variables, and appropriate regression models were trained. The performed experiments show that XGBoost is the most accurate, achieving MAE of 8.9 for Center, 8.9 for Karpos and 7.3 for Kumanovo municipality for the PM10 pollutant. The improvements over the baseline, Dummy regressor are significant, reducing the MAE for 12 on average.|
|Open Access:||Open access publication|
|Appears in Collections:||International Conference on Applied Innovations in IT (ICAIIT)|
Files in This Item:
|2_2_Trenchevski.pdf||Prediction of Air Pollution Concentration Using Weather Data and Regression Models||1.71 MB||Adobe PDF|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.