SVR machine learning and SARIMA-based air quality index classification and forecasting system

• Published in: Discover applied sciences Vol. 7; no. 9; p. 994
• Main Authors: Bahadure, Nilesh Bhaskarrao, Gonge, Sudhanshu, Parashar, Deepak, Shah, Bhoomi, Patil, Prasenjeet Damodar, Renugadevi, M., Raju, Nagrajan
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 26.08.2025; Springer Nature B.V
• Subjects: Accuracy; Air monitoring; Air pollution; Air quality; Algorithms; Applied and Technical Physics; Artificial intelligence; Autoregressive moving-average models; Chemistry/Food Science; Classification; Climate change; Clustering; Confidence intervals; Decision making; Earth Sciences; Ecosystems; Engineering; Environment; Environmental monitoring; International organizations; Learning algorithms; Machine learning; Materials Science; Nitrogen dioxide; Outdoor air quality; Perceptions; Performance enhancement; Pollutants; Pollution control; Pollution levels; Predictions; Public awareness; Quality management; Recall; Statistical analysis; Support vector machines; Sustainable development; Trends; VOCs; Volatile organic compounds; Seasonal autoregressive integrated moving average (SARIMA); Support vector regression; Time-series; Air quality index (AQI); Machine learning

The prediction and classification of the air quality index (AQI) are a primary concern. Still, due to the involvement of many parameters affecting AQI pre- dictions, it becomes time-consuming and monotonous work. The prediction and classification of the AQI with utmost accuracy is a pivotal tool for...