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AirQFormer: Improving Regional Air Quality Forecast with a Hybrid Deep Learning Model

This repository includes the deep learning model developed in the study AirQFormer: Improving Regional Air Quality Forecast with a Hybrid Deep Learning Model , which is an end-to-end bias correction method to improve the accuracy of regional CMAQ forecasts. The baseline LSTM model is also included.

By Hu, Mingyun, Lu, Xingcheng, Chen, Yiang, Li, Zhenning, Wang, Yueya, Fung, Jimmy C.H.ToolReference information

Resource information

Format
zip
Original repository
14064407
DOI
10.5281/zenodo.14064407
Credit basis
Creators in Zenodo record metadata
Access
Open files available from the original Zenodo deposit
Last checked
Nov 5, 2026
License
cc-by-4.0
Last record update
Nov 5, 2026

About this resource

This repository includes the deep learning model developed in the study AirQFormer: Improving Regional Air Quality Forecast with a Hybrid Deep Learning Model , which is an end-to-end bias correction method to improve the accuracy of regional CMAQ forecasts. The baseline LSTM model is also included.