Auto-Modal: Air-Quality Index Forecasting with Modal Decomposition Attention

Abstract

The air-quality index (AQI) is an important comprehensive evaluation index to measure the quality of air, with its value reflecting the degree of air pollution. However, it is difficult to predict the AQI accurately by the commonly used WRF-CMAQ model due to the uncertainty of the simulated meteorological field and emission inventory. In this paper, a novel Auto-Modal network with Attention Mechanism (AMAM) has been proposed to predict the hourly AQI with a structure of dual input path. The first path is based on bidirectional encoder representation from the transformer to predict the AQI with the historical measured meteorological data and pollutants. The other path is a baseline to improve the generalization ability based on predicting the AQI by the WRF-CMAQ model. Several experiments were undertaken to evaluate the performance of the proposed model, with the results showing that the auto-modal network achieves a superior performance for all prediction lengths compared to some state-of-the-art models.

Keywords

• modal decomposition
• air quality
• short-term forecast
• bidirectional encoder representation from the transformer