(Submitted on 27 Mar 2020)

Abstract: Urban air pollution has become a major environmental problem that threatens
public health. It has become increasingly important to infer fine-grained urban
air quality based on existing monitoring stations. One of the challenges is how
to effectively select some relevant stations for air quality inference. In this
paper, we propose a novel model based on reinforcement learning for urban air
quality inference. The model consists of two modules: a station selector and an
air quality regressor. The station selector dynamically selects the most
relevant monitoring stations when inferring air quality. The air quality
regressor takes in the selected stations and makes air quality inference with
deep neural network. We conduct experiments on a real-world air quality dataset
and our approach achieves the highest performance compared with several popular
solutions, and the experiments show significant effectiveness of proposed model
in tackling problems of air quality inference.