GreenEyes: An Air Quality Evaluating Model based on WaveNet

TL;DR

GreenEyes is a deep neural network model combining WaveNet and LSTM with attention for accurate air quality prediction, aiding policy and daily decision-making.

Contribution

The paper introduces GreenEyes, a novel deep learning model integrating WaveNet and LSTM with attention for improved air quality forecasting.

Findings

Effective prediction of air quality levels demonstrated

Model outperforms baseline methods in accuracy

Public dataset and code released for reproducibility

Abstract

Accompanying rapid industrialization, humans are suffering from serious air pollution problems. The demand for air quality prediction is becoming more and more important to the government's policy-making and people's daily life. In this paper, We propose GreenEyes -- a deep neural network model, which consists of a WaveNet-based backbone block for learning representations of sequences and an LSTM with a Temporal Attention module for capturing the hidden interactions between features of multi-channel inputs. To evaluate the effectiveness of our proposed method, we carry out several experiments including an ablation study on our collected and preprocessed air quality data near HKUST. The experimental results show our model can effectively predict the air quality level of the next timestamp given any segment of the air quality data from the data set. We have also released our standalone dataset at https://github.com/AI-Huang/IAQI_Dataset The model and code for this paper are publicly available at https://github.com/AI-Huang/AirEvaluation