mlsanityData to 7 Oct 2026

PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting

arXiv 2002.12898 ↗First posted 10 Feb 2020eess.SP cs.LG eess.IVcited by 52

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Abstract (arXiv)

When predicting PM2.5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period. In this paper, we identify a set of critical domain knowledge for PM2.5 forecasting and develop a novel graph based model, PM2.5-GNN, being capable of capturing long-term dependencies. On a real-world dataset, we validate the effectiveness of the proposed model and examine its abilities of capturing both fine-grained and long-term influences in PM2.5 process. The proposed PM2.5-GNN has also been deployed online to provide free forecasting service.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Feb 2020.

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Citing papers per year: 2021: 3; 2022: 5; 2023: 10; 2024: 12; 2025: 11; 2026: 11.

52 papers.

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  1. 2022Graph Neural Networks for Graphs with Heterophily: A Surveycited by 175
  2. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  3. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
  4. 2022AirFormer: Predicting Nationwide Air Quality in China with Transformerscited by 73
  5. 2024Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlookcited by 33
  6. 2021Group-Aware Graph Neural Network for Nationwide City Air Quality Forecastingcited by 32
  7. 2023Foundation Models for Weather and Climate Data Understanding: A Comprehensive Surveycited by 32
  8. 2024A Survey of Deep Learning and Foundation Models for Time Series Forecastingcited by 32
  9. 2022Graph Neural Networks in IoT: A Surveycited by 27
  10. 2024AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Predictioncited by 25
  11. 2024Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systemscited by 25
  12. 2024STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Modelcited by 24
38 more
  1. 2023Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stationscited by 18
  2. 2022Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attentioncited by 15
  3. 2025Deep Learning and Foundation Models for Weather Prediction: A Surveycited by 15
  4. 2025Learning with Calibration: Exploring Test-Time Computing of Spatio-Temporal Forecastingcited by 9
  5. 2023Machine Learning for Urban Air Quality Analytics: A Surveycited by 8
  6. 2025ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecastingcited by 8
  7. 2023Graph Neural Network for spatiotemporal data: methods and applicationscited by 6
  8. 2025PCDCNet: A Surrogate Model for Air Quality Forecasting with Physical-Chemical Dynamics and Constraintscited by 5
  9. 2021Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimationcited by 4
  10. 2024FairSTG: Countering performance heterogeneity via collaborative sample-level optimizationcited by 3
  11. 2024Beyond Grid Data: Exploring Graph Neural Networks for Earth Observationcited by 3
  12. 2025Spatiotemporal Causal Decoupling Model for Air Quality Forecastingcited by 3
  13. 2021Deep-AIR: A Hybrid CNN-LSTM Framework for Air Quality Modeling in Metropolitan Citiescited by 2
  14. 2022Graph Signal Reconstruction Techniques for IoT Air Pollution Monitoring Platformscited by 2
  15. 2024Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blockscited by 2
  16. 2024Decomposing weather forecasting into advection and convection with neural networkscited by 2
  17. 2023Simulating the Air Quality Impact of Prescribed Fires Using Graph Neural Network-Based PM2.5 Forecastscited by 1
  18. 2025STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Predictioncited by 1
  19. 2025A Causality-Aware Spatiotemporal Model for Multi-Region and Multi-Pollutant Air Quality Forecastingcited by 1
  20. 2026Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecastingcited by 1
  21. 2026A General ReLearner: Empowering Spatiotemporal Prediction by Re-learning Input-label Residualcited by 1
  22. 2026AirDDE: Multifactor Neural Delay Differential Equations for Air Quality Forecastingcited by 1
  23. 2026AirQualityBench: A Realistic Evaluation Benchmark for Global Air Quality Forecastingcited by 1
  24. 2023Dynamic Graph Neural Network with Adaptive Edge Attributes for Air Quality Predictionscited by 0
  25. 2023Spatiotemporal Graph Convolutional Recurrent Neural Network Model for Citywide Air Pollution Forecastingcited by 0
  26. 2023Multi-spatial Multi-temporal Air Quality Forecasting with Integrated Monitoring and Reanalysis Datacited by 0
  27. 2024ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolutioncited by 0
  28. 2024HiMoE: Heterogeneity-Informed Mixture-of-Experts for Fair Spatial-Temporal Forecastingcited by 0
  29. 2024Spatio-Temporal Forecasting of PM2.5 via Spatial-Diffusion guided Encoder-Decoder Architecturecited by 0
  30. 2025Air Quality Prediction with A Meteorology-Guided Modality-Decoupled Spatio-Temporal Networkcited by 0
  31. 2025A Network Science Approach to Granular Time Series Segmentationcited by 0
  32. 2025Double-Diffusion: Balancing Speed, Accuracy, and Uncertainty in Probabilistic Forecasting for Urban Sensor Networkscited by 0
  33. 2025Physics-Guided Inductive Spatiotemporal Kriging for PM2.5 with Satellite Gradient Constraintscited by 0
  34. 2026NEXUS: A compact neural architecture for high-resolution spatiotemporal air quality forecasting in Delhi National Capital Regioncited by 0
  35. 2026Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learningcited by 0
  36. 2026Earth Science Foundation Models: From Perception to Reasoning and Discoverycited by 0
  37. 2026MVG-KAN: Multi-View Geo-Wind Guided KAN for PM2.5 Forecastingcited by 0
  38. 2026Every Client Is an Environment: Federated De-confounding for Spatio-Temporal Forecastingcited by 0

50 most cited of 52 All 52 (JSON)