International Journal on Science and Technology

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Improving Air Quality Prediction Using Gradient Boosting

Author(s) Abhinav Balasubramanian
Country United States
Abstract Air quality prediction plays a crucial role in safeguarding public health. Accurate forecasting of air quality indices (AQI) is essential for mitigating health risks and promoting environmental sustainability. Existing prediction models often struggle with capturing the complex interactions among meteorological and pollutant variables, resulting in limited accuracy and reliability.
This paper presents a theoretical framework that employs Gradient Boosting, a robust ensemble learning technique, to enhance the predictive capabilities of air quality models. The framework leverages key environmental features to address the challenges of pattern recognition and data variability, aiming for improved performance and adaptability across diverse conditions.
The proposed approach holds potential for developing advanced monitoring systems and reducing the adverse impacts of air pollution. By addressing the limitations of traditional methodologies, this work highlights a promising pathway for more precise and actionable air quality predictions.
Keywords Artificial Intelligence (AI), Air Quality Prediction, Environmental Forecasting, Gradient Boosting.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 13, Issue 2, April-June 2022
Published On 2022-06-07
Cite This Improving Air Quality Prediction Using Gradient Boosting - Abhinav Balasubramanian - IJSAT Volume 13, Issue 2, April-June 2022. DOI 10.5281/zenodo.14631450
DOI https://doi.org/10.5281/zenodo.14631450
Short DOI https://doi.org/g8zdqq
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