MVAR模型实现多污染物长时序预报，提升数据利用效率。
MVAR: MultiVariate AutoRegressive Air Pollutants Forecasting Model
- 基于自回归机制，减少对长时间输入的依赖
- 支持120小时长期预测，优于现有方法
- 融合气象数据，捕捉污染物空间交互
空气污染物对环境和人类健康构成严重威胁，准确预测污染物浓度对污染预警和政策制定至关重要。现有研究多聚焦单污染物预测，忽视不同污染物间的相互作用及其多样化的空间响应。为满足多污染物预报的实际需求，我们提出多变量自回归空气污染物预测模型（MVAR），降低对长时窗输入的依赖，提升数据利用效率。设计了多变量自回归训练范式，使MVAR可实现120小时的长期序列预测。此外，MVAR引入气象耦合空间注意力模块，灵活结合基于AI的气象预报，同时学习污染物间的相互作用及其空间差异响应。针对空气质量预测缺乏标准化数据集的问题，构建了涵盖华北75个城市2018至2023年6种主要污染物的综合性数据集，包含ERA5再分析数据与FuXi-2.0预报数据。实验结果表明，所提模型显著优于现有先进方法，验证了其架构的有效性。
原文摘要 · Abstract (English)
Air pollutants pose a significant threat to the environment and human health, thus forecasting accurate pollutant concentrations is essential for pollution warnings and policy-making. Existing studies predominantly focus on single-pollutant forecasting, neglecting the interactions among different pollutants and their diverse spatial responses. To address the practical needs of forecasting multivariate air pollutants, we propose MultiVariate AutoRegressive air pollutants forecasting model (MVAR), which reduces the dependency on long-time-window inputs and boosts the data utilization efficiency. We also design the Multivariate Autoregressive Training Paradigm, enabling MVAR to achieve 120-hour long-term sequential forecasting. Additionally, MVAR develops Meteorological Coupled Spatial Transformer block, enabling the flexible coupling of AI-based meteorological forecasts while learning the interactions among pollutants and their diverse spatial responses. As for the lack of standardized datasets in air pollutants forecasting, we construct a comprehensive dataset covering 6 major pollutants across 75 cities in North China from 2018 to 2023, including ERA5 reanalysis data and FuXi-2.0 forecast data. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods and validate the effectiveness of the proposed architecture.
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