GreenEyes: An Air Quality Evaluating Model based on WaveNet
This addresses air pollution prediction for government policy-making and daily life, but it is incremental as it applies existing methods to a new dataset.
The authors tackled air quality prediction by proposing GreenEyes, a deep neural network model combining WaveNet and LSTM with Temporal Attention, which effectively predicts air quality levels for the next timestamp on a collected dataset near HKUST.
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