Work

PM2.5-GNN

Year: 2020

Type: conference-paper

Abstract: When predicting PM2.5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period. In this paper, we identify a set of critical domain knowledge for PM2.5 forecasting and develop a novel graph based model, PM2.5-GNN, being capable of capturing long-term dependencies. On a real-world dataset, we validate the effectiveness of the proposed model and examine its abilities of capturing both fine-grained and long-term influences in PM2.5 process. The proposed PM2.5-GNN has also been deployed online to provide free forecasting service.

Source: ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS)

DOI: 10.1145/3397536.3422208

Authors: Shuo Wang, Yanran Li, Jiang Zhang, Qingye Meng, Lingwei Meng, Fei Gao

Institutions: Beijing Normal University, Hong Kong Polytechnic University, Princeton University

Language: English

Topic: Air Quality Monitoring and Forecasting

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

Open Access status: green

Funder: Foundation for Innovative Research Groups of the National Natural Science Foundation of China

Award: G105075403