mlsanityData to 14 Oct 2026

Air Quality Prediction with A Meteorology-Guided Modality-Decoupled Spatio-Temporal Network

arXiv 2504.10014 ↗First posted 14 Apr 2025cs.LG cs.AI cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Air quality prediction plays a crucial role in public health and environmental protection. Accurate air quality prediction is a complex multivariate spatiotemporal problem, that involves interactions across temporal patterns, pollutant correlations, spatial station dependencies, and particularly meteorological influences that govern pollutant dispersion and chemical transformations. Existing works underestimate the critical role of atmospheric conditions in air quality prediction and neglect comprehensive meteorological data utilization, thereby impairing the modeling of dynamic interdependencies between air quality and meteorological data. To overcome this, we propose MDSTNet, an encoder-decoder framework that explicitly models air quality observations and atmospheric conditions as distinct modalities, integrating multi-pressure-level meteorological data and weather forecasts to capture atmosphere-pollution dependencies for prediction. Meantime, we construct ChinaAirNet, the first nationwide dataset combining air quality records with multi-pressure-level meteorological observations. Experimental results on ChinaAirNet demonstrate MDSTNet's superiority, substantially reducing 48-hour prediction errors by 17.54% compared to the state-of-the-art model. The source code and dataset will be available on github.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 52 references that are in the map. First posted on arXiv 14 Apr 2025.

Builds on

19 of 52 references.

References · 19

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  4. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  5. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  6. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 931
  7. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 434
  8. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 333
  9. 2023Long-term Forecasting with TiDE: Time-series Dense Encodercited by 332
  10. 2021Rethinking and Improving Relative Position Encoding for Vision Transformercited by 207
  11. 2022AirFormer: Predicting Nationwide Air Quality in China with Transformerscited by 73
  12. 2022Multivariate Time Series Forecasting with Dynamic Graph Neural ODEscited by 72
7 more
  1. 2020PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecastingcited by 55
  2. 2020Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networkscited by 39
  3. 2018Deep Air Quality Forecasting Using Hybrid Deep Learning Frameworkcited by 33
  4. 2021Group-Aware Graph Neural Network for Nationwide City Air Quality Forecastingcited by 32
  5. 2024AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Predictioncited by 26
  6. 2024Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systemscited by 26
  7. 2017Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-grained Air Qualitycited by 16

Built on by

Not cited yet.