AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference

• URL: http://arxiv.org/abs/2003.12205v1
• Date: Fri, 27 Mar 2020 02:04:00 GMT
• Title: AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference
• Authors: Huiqiang Zhong and Cunxiang Yin and Xiaohui Wu and Jinchang Luo and JiaWei He
• Abstract summary: Urban air pollution has become a major environmental problem that threatens public health. One of the challenges is how to effectively select some relevant stations for air quality inference. We propose a novel model based on reinforcement learning for urban air quality inference.
• Score: 7.238981927352622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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