{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pm25-gnn-a-domain-knowledge-enhanced-graph","title":"PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting","arxiv_id":"2002.12898","date":"2020-02-10","proceeding":"ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2020 11","authors":["Shuo Wang","Yan-ran Li","Jiang Zhang","Qingye Meng","Lingwei Meng","Fei Gao"],"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.","url_abs":"https://arxiv.org/abs/2002.12898v2","url_pdf":"https://arxiv.org/pdf/2002.12898v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pm25-gnn-a-domain-knowledge-enhanced-graph","repo_url":"https://github.com/shuowang-ai/PM2.5-GNN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"pm25-gnn-a-domain-knowledge-enhanced-graph","repo_url":"https://github.com/shawnwang-tech/PM2.5-GNN","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2002.12898","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}