Rapid urbanization, socio-economic development, and industrialization result in serious deterioration in air quality (AQ). For environmental management, prediction can be a mitigation and regulation for an adaptation method. This paper applies a supervised machine-learning (ML) technique, decision tree (DT), to predict AQ. In Matlab 2018b, AQ values and climate variables (temperature, relative humidity, wind speed, and rainfall) classify categorical AQ. For eight divisions in Bangladesh, AQ datasets are obtained from the Department of Environment (DOE), while weather variables are acquired from the National Aeronautics Space Administration (NASA)-Prediction of Worldwide Energy Resources (POWER) project. The experiments include daily observations for seven years (2014 to 2020) indicating an average unhealthy AQ (65 to 75% per year) among the chosen metropolitans. DT as a predictive model, datasets from Dhaka are utilized in training (80%) and validation (20%) resulting in an accuracy of 98.8%. This model further is applied to forecast monthly AQ for Chittagong and found predictability ≥97%. Finally, AQ is predicted and found 96% accuracy for eight cities (year: 2020). The investigations encourage providing AQ alerts to the public mostly among data-sparse regions.
|Title of host publication||2021 5th International Conference on Electrical Information and Communication Technology, EICT 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - 2021|
|Event||5th International Conference on Electrical Information and Communication Technology, EICT 2021 - Khulna, Bangladesh|
Duration: 17 Dec 2021 → 19 Dec 2021
|Name||2021 5th International Conference on Electrical Information and Communication Technology, EICT 2021|
|Conference||5th International Conference on Electrical Information and Communication Technology, EICT 2021|
|Period||17/12/21 → 19/12/21|
Bibliographical noteFunding Information:
M. Hussain sincerely thanks Ewha Womans University and Samsung Foundation for the support through the Ewha Global Partnership Program and the Samsung Dream Fellowship, respectively. S. K. Park is supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1A6A1A08025520).
© 2021 IEEE.
- Air quality
- Decision tree algorithm
- Supervised machine-learning