Spatiotemporal Causal Decoupling Model for Air Quality Forecasting

TL;DR

This paper introduces AirCade, a novel spatiotemporal causal decoupling model for air quality forecasting that improves accuracy by disentangling causal relationships and explicitly modeling future meteorological uncertainties.

Contribution

The paper proposes a new causal decoupling approach with a spatiotemporal module and causal intervention, advancing air quality prediction accuracy and robustness.

Findings

Achieves over 20% relative improvement over state-of-the-art models.

Effectively disentangles synchronous and past causal influences.

Enhances robustness through explicit modeling of meteorological uncertainty.

Abstract

Due to the profound impact of air pollution on human health, livelihoods, and economic development, air quality forecasting is of paramount significance. Initially, we employ the causal graph method to scrutinize the constraints of existing research in comprehensively modeling the causal relationships between the air quality index (AQI) and meteorological features. In order to enhance prediction accuracy, we introduce a novel air quality forecasting model, AirCade, which incorporates a causal decoupling approach. AirCade leverages a spatiotemporal module in conjunction with knowledge embedding techniques to capture the internal dynamics of AQI. Subsequently, a causal decoupling module is proposed to disentangle synchronous causality from past AQI and meteorological features, followed by the dissemination of acquired knowledge to future time steps to enhance performance. Additionally, we introduce a causal intervention mechanism to explicitly represent the uncertainty of future meteorological features, thereby bolstering the model's robustness. Our evaluation of AirCade on an open-source air quality dataset demonstrates over 20\% relative improvement over state-of-the-art models.