# Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks

arXiv: https://arxiv.org/abs/2012.15037 · HTML: https://mlsanity.com/paper/2012.15037/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2020.

## Built on by (most cited first, 30 of 39)

- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting](https://mlsanity.com/paper/2302.14829/) (2023)
- [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://mlsanity.com/paper/2312.03014/) (2023)
- [DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting](https://mlsanity.com/paper/2203.07681/) (2022)
- [Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models](https://mlsanity.com/paper/2210.02447/) (2022)
- [W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting](https://mlsanity.com/paper/2304.08754/) (2023)
- [AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction](https://mlsanity.com/paper/2402.03784/) (2024)
- [Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.07359/) (2021)
- [MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal](https://mlsanity.com/paper/2107.05180/) (2021)
- [PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction](https://mlsanity.com/paper/2309.09500/) (2023)
- [Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling](https://mlsanity.com/paper/2201.06814/) (2022)
- [Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference](https://mlsanity.com/paper/2109.09506/) (2021)
- [Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution](https://mlsanity.com/paper/2301.02068/) (2023)
- [MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent Imputation](https://mlsanity.com/paper/2408.05740/) (2024)
- [AutoSTL: Automated Spatio-Temporal Multi-Task Learning](https://mlsanity.com/paper/2304.09174/) (2023)
- [MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer](https://mlsanity.com/paper/2403.09223/) (2024)
- [Machine Learning for Urban Air Quality Analytics: A Survey](https://mlsanity.com/paper/2310.09620/) (2023)
- [Graph Convolutional Networks for Traffic Forecasting with Missing Values](https://mlsanity.com/paper/2212.06419/) (2022)
- [IN-Flow: Instance Normalization Flow for Non-stationary Time Series Forecasting](https://mlsanity.com/paper/2401.16777/) (2024)
- [A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint Prediction](https://mlsanity.com/paper/2401.17838/) (2024)
- [NRFormer: Nationwide Nuclear Radiation Forecasting with Spatio-Temporal Transformer](https://mlsanity.com/paper/2410.11924/) (2024)
- [Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications](https://mlsanity.com/paper/2507.00914/) (2025)
- [Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting](https://mlsanity.com/paper/2505.14555/) (2025)
- [A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling](https://mlsanity.com/paper/2206.00007/) (2022)
- [RePST: Language Model Empowered Spatio-Temporal Forecasting via Semantic-Oriented Reprogramming](https://mlsanity.com/paper/2408.14505/) (2024)
- [HSTFL: A Heterogeneous Federated Learning Framework for Misaligned Spatiotemporal Forecasting](https://mlsanity.com/paper/2409.18482/) (2024)
- [Robust Multivariate Time Series Forecasting against Intra- and Inter-Series Transitional Shift](https://mlsanity.com/paper/2407.13194/) (2024)
- [DAMBench: A Multi-Modal Benchmark for Deep Learning-based Atmospheric Data Assimilation](https://mlsanity.com/paper/2511.01468/) (2025)
- [AirDDE: Multifactor Neural Delay Differential Equations for Air Quality Forecasting](https://mlsanity.com/paper/2603.17529/) (2026)

## Abstract (arXiv)

Accurate and timely air quality and weather predictions are of great importance to urban governance and human livelihood. Though many efforts have been made for air quality or weather prediction, most of them simply employ one another as feature input, which ignores the inner-connection between two predictive tasks. On the one hand, the accurate prediction of one task can help improve another task's performance. On the other hand, geospatially distributed air quality and weather monitoring stations provide additional hints for city-wide spatiotemporal dependency modeling. Inspired by the above two insights, in this paper, we propose the Multi-adversarial spatiotemporal recurrent Graph Neural Networks (MasterGNN) for joint air quality and weather predictions. Specifically, we first propose a heterogeneous recurrent graph neural network to model the spatiotemporal autocorrelation among air quality and weather monitoring stations. Then, we develop a multi-adversarial graph learning framework to against observation noise propagation introduced by spatiotemporal modeling. Moreover, we present an adaptive training strategy by formulating multi-adversarial learning as a multi-task learning problem. Finally, extensive experiments on two real-world datasets show that MasterGNN achieves the best performance compared with seven baselines on both air quality and weather prediction tasks.