{"type":"ScholarlyArticle","title":"CHAM-net: A Contrastive Hierarchical Adaptive Meta-network for Robust Global Methane Flux Prediction","authors":[{"name":"Rongchao Dong","slug":"rongchao-dong","given":"Rongchao","family":"Dong","affiliation":""},{"name":"Yiming Sun","slug":"yiming-sun","given":"Yiming","family":"Sun","affiliation":""},{"name":"Shuo Chen","slug":"shuo-chen","given":"Shuo","family":"Chen","affiliation":""},{"name":"Youmi Oh","slug":"youmi-oh","given":"Youmi","family":"Oh","affiliation":""},{"name":"Licheng Liu","slug":"licheng-liu","given":"Licheng","family":"Liu","affiliation":""},{"name":"Yiqun Xie","slug":"yiqun-xie","given":"Yiqun","family":"Xie","affiliation":""},{"name":"Xiaowei Jia","slug":"xiaowei-jia","given":"Xiaowei","family":"Jia","affiliation":""}],"abstract":"Methane is a potent greenhouse gas that significantly contributes to global warming. However, accurately estimating global methane emissions and consumption remains challenging due to the complex interactions among environmental drivers that may vary across spatial and temporal scales. Prior data-driven methods often overlook the inherent spatiotemporal heterogeneity of ecosystems, failing to explicitly capture site-specific characteristics and cross-year evolutionary dynamics. To address these issues, we propose the Contrastive Hierarchical Adaptive Meta-network (CHAM-net), a novel framework that explicitly learns from historical context to capture site-specific dynamics. CHAM-net employs a hierarchical encoder–decoder architecture, in which the encoder captures site-specific characteristics from historical data and then dynamically conditions the decoder to generate the final prediction. Experimental results demonstrate that CHAM-net consistently outperforms all baseline methods on both simulation and observational datasets for methane emission and consumption, achieving nRMSE values as low as 0.43 and 0.88 with corresponding R^2 scores up to 0.97 and 0.68 for emission prediction.","doi":"10.24963/ijcai.2026/793","url":"https://ijcai.org/proceedings/2026/793","pdf":"https://ijcai.org/proceedings/2026/0793.pdf","markdown":"https://ijcai.org/proceedings/2026/793.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/793","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Social Good","track":"AI and Social Good","number":793,"pages":{"first":7129,"last":7137},"keywords":["Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications","Data Mining: Data Mining","Machine Learning: Machine Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}