A Sequence-to-Sequence Air Quality Predictor Based on the n-Step Recurrent Prediction

Abstract

Increasingly, more people are suffering from the effects of air pollution. This study took Beijing as an example and proposed an attention-based air quality predictor (AAQP) that could better protect people from air pollution. The AAQP is a seq2seq model, and it exploits historical air quality data and weather data to predict future air quality indexes. Although existing research has promoted seq2seq for air quality prediction, there are still two problems. First, the seq2seq has a slow training speed so the original RNN in the encoder was replaced with a fully connected encoder to accelerate the training process. Position embedding was also introduced to help the fully connected encoder find the sequential relationships among source sequences. Another problem is error accumulation caused by recurrent prediction. Accordingly, the n-step recurrent prediction was proposed to solve this problem. The experimental results validated that the AAQP with n-step recurrent prediction had better performance than the related arts since the error accumulation was reduced, and the training time was significantly decreased compared with the original seq2seq attention model.