Air quality prediction and control systems using machine learning and adaptive neuro-fuzzy inference system
Pouya Mottahedin,
Benyamin Chahkandi,
Reza Moezzi,
Amir M. Fathollahi-Fard,
Mojtaba Ghandali,
Mohammad Gheibi
Affiliations
Pouya Mottahedin
Department of Chemical Engineering, Faculty of Engineering, University of Garmsar, Garmsar, Iran
Benyamin Chahkandi
Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Narutowicza Street 11/12, 80-233, Gdansk, Poland
Reza Moezzi
Faculty of Mechatronics, Informatics and Interdisciplinary Studies, Technical University of Liberec, 461 17, Liberec, Czech Republic; Association of Talent under Liberty in Technology (TULTECH), Sopruse Pst, 10615, Tallinn, Estonia
Amir M. Fathollahi-Fard
Département d′Analytique, Opérations et Technologies de l′Information, Université du Québec à Montréal, B.P. 8888, Succ. Centre-ville, Montréal, QC, H3C 3P8, Canada; Corresponding author. Département d'Analytique, Opérations et Technologies de l'Information, Université du Québec à Montréal, B.P. 8888, Succ. Centre-ville, Montréal, QC, H3C 3P8, Canada.
Mojtaba Ghandali
Environment Research Center, Department of Environment, Semnan University, Semnan, Iran
Mohammad Gheibi
Institute for Nanomaterials, Advanced Technologies and Innovation, Technical University of Liberec, 461 17, Liberec, Czech Republic
Accurately predicting air quality concentrations is a challenging task due to the complex interactions of pollutants and their reliance on nonlinear processes. This study introduces an innovative approach in environmental engineering, employing artificial intelligence techniques to forecast air quality in Semnan, Iran. Comprehensive data on seven different pollutants was initially collected and analyzed. Then, several machine learning (ML) models were rigorously evaluated for their performance, and a detailed analysis was conducted. By incorporating these advanced technologies, the study aims to create a reliable framework for air quality prediction, with a particular focus on the case study in Iran. The results indicated that the adaptive neuro-fuzzy inference system (ANFIS) was the most effective method for predicting air quality across different seasons, showing high reliability across all datasets.
