Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam

• Main Authors: Ravindiran G., Hayder G., Kanagarathinam K., Alagumalai A., Sonne C.
• Other Authors: 57226345669
• Format: Article
• Published: Elsevier Ltd 2024
• Subjects: Air quality index; Climate action; Gaseous pollutants; Meteorological parameters; Particulate matter; Air Pollutants; Air Pollution; Cities; Environmental Monitoring; Humans; Machine Learning; Particulate Matter; Andhra Pradesh; India; Visakhapatnam; Air quality; Climate models; Errors; Fog; Forecasting; Forestry; Machine learning; Mean square error; Particles (particulate matter); Population statistics; Air quality indices; Air quality prediction; Correlation coefficient; Machine learning models; Machine-learning; air quality; ambient air; atmospheric pollution; industrialization; meteorology; spatiotemporal analysis; urban pollution; air monitoring; air pollution; Article; artificial neural network; climate; correlation coefficient; data analysis; death toll; human; learning algorithm; machine learning; mean absolute error; mean squared error; particulate matter; root mean squared error; air pollutant; city; environmental monitoring; procedures; Adaptive boosting

Clean air is critical component for health and survival of human and wildlife, as atmospheric pollution is associated with a number of significant diseases including cancer. However, due to rapid industrialization and population growth, activities such as transportation, household, agricultural, and...