Analysis of Regional Air Quality Data using Extreme Value Theory and Deep Learning Models.

Accurate air quality forecasting is crucial for managing pollution and protecting public health, especially in rapidly growing urban environments. This work presents an integrated framework combining extreme value theory with Transformer-based deep learning models to analyze and forecast particulate matter dynamics. The approach captures both long-term risks of extreme pollution and short-term temporal patterns.

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