TL;DR
A hybrid machine-learning framework for AQI prediction in Kağıthane, Istanbul, Türkiye, using a long-term dataset spanning approximately ten years, thereby reducing reliance on dense sensor infrastructures and achieving overall accuracy of approximately 98%, enabling timely health advisories and more efficient allocation of air-quality management resources.
An iterative, multi-model machine learning workflow to reconstruct missing daily pollution data for all six pollutants across 45 stations from 2014–2024 supports the usefulness of the approach for long-term regional reconstruction while also highlighting its limitations for pollutants with strong local emission signatu...
Air pollution in Delhi has reached dangerous levels, necessitating accurate and dependable Air Quality Index (AQI) forecasts for timely intervention. However, traditional monitoring methods and stand-alone machine learning algorithms frequently fail to capture the complex, nonlinear relationships between contaminants a...
A Temporal-Aware Multi-Task Learning (TMTL-AQI) framework to assess urban air quality via structured data that outperforms single-task and baseline multi-task models with an accuracy of 0.7605 and an F1-score of 0.7422.
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