Ensemble stacking of machine learning models for air quality prediction for Hyderabad city in India
Gokulan Ravindiran, K. Karthick, Sivarethinamohan Rajamanickam, Deepshikha Datta, Bimal Das, G. Shyamala, Gasim Hayder, Azees Maria
Abstract
= 0.999) accuracy with low error metrics (mean absolute error [MAE] = 0.496, mean square error [MSE] = 0.429, root-mean-square error [RMSE] = 0.655). These results highlight the efficacy of ensemble stacking for AQI prediction, providing crucial information for policymakers to formulate strategies to reduce air pollution's effects on public health and environmental sustainability.
Topics & Concepts
Ensemble learningStackingQuality (philosophy)Air quality indexComputer scienceMachine learningArtificial intelligenceChemistryGeographyPhysicsMeteorologyOrganic chemistryQuantum mechanicsAir Quality Monitoring and ForecastingAir Quality and Health Impacts