Optimizing Air Pollution Forecasting Models Through Knowledge Distillation: A Novel GCN and TRANS_GRU Methodology for Indian Cities

Optimizing Air Pollution Forecasting Models Through Knowledge Distillation: A Novel GCN and TRANS_GRU Methodology for Indian Cities

As air pollution remains a pressing issue affecting environmental and public health, precise forecasting and monitoring are essential. Machine learning and deep learning models are increasingly applied in air pollution studies, using their advanced capabilities to decode intricate pollutant data. A...

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Bibliographic Details
Main Authors: Sudhir Kumar, Vaneet Kour, Ankit Raj, Tagru Tapung, Shivendu Mishra, Rajiv Misra, T. N. Singh
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Air quality forecasting
computational efficiency
graph convolutional networks
knowledge distillation
PM2.5
transformer models
Online Access:https://ieeexplore.ieee.org/document/10906570/
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