ArticleiScience2025
Ensemble stacking of machine learning models for air quality prediction for Hyderabad city in India.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Drought development characteristics modulate global dry-wet abrupt alternation through distinct land-atmosphere feedbacks.Nature communications · 2026Article
- Particulate matter (PM2.5 and PM10) prediction using fourier series decomposition in combination with LSTM and SVM.Scientific reports · 2026Article
- Accurate AQI forecasting in a high-altitude city using a simulated CVOCA-BiLSTM hybrid model: a case study of Lhasa, Tibet.Scientific reports · 2025Article
- Understanding ozone variability in spatial responses to emissions and meteorology in China using interpretable machine learning.iScience · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hyderabad, one of the rapidly developing cities in India, is facing with severe air pollution due to rapid urbanization, industrial operations, and climatic factors. To alleviate the negative impact on human health and the environment, accurate monitoring and forecasting of air quality are essential. This research utilized various machine learning models, such as XGBoost, LarsCV, Bayesian Ridge, AdaBoost, and ensemble stacking methods, to forecast the air quality index (AQI) using data from August 2016 to October 2023, which included 18 different air pollutants, including meteorological parameters. The ensemble stacking method showed excellent performance, attaining high training (R
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