CHAM-net: A Contrastive Hierarchical Adaptive Meta-network for Robust Global Methane Flux Prediction

Dong, Rongchao; Sun, Yiming; Chen, Shuo; Oh, Youmi; Liu, Licheng; Xie, Yiqun; Jia, Xiaowei

doi:10.24963/ijcai.2026/793

CHAM-net: A Contrastive Hierarchical Adaptive Meta-network for Robust Global Methane Flux Prediction

Rongchao Dong, Yiming Sun, Shuo Chen, Youmi Oh, Licheng Liu, Yiqun Xie, Xiaowei Jia

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. AI and Social Good. Pages 7129-7137.

https://doi.org/10.24963/ijcai.2026/793

Keywords

Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications
Data Mining: Data Mining
Machine Learning: Machine Learning

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.

Rongchao Dong, Yiming Sun, Shuo Chen, Youmi Oh, Licheng Liu, Yiqun Xie, and Xiaowei Jia. 2026. CHAM-net: A Contrastive Hierarchical Adaptive Meta-network for Robust Global Methane Flux Prediction. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 7129–7137. https://doi.org/10.24963/ijcai.2026/793

@inproceedings{ijcai2026p793,
  title     = {CHAM-net: A Contrastive Hierarchical Adaptive Meta-network for Robust Global Methane Flux Prediction},
  author    = {Dong, Rongchao and Sun, Yiming and Chen, Shuo and Oh, Youmi and Liu, Licheng and Xie, Yiqun and Jia, Xiaowei},
  booktitle = {Proceedings of the Thirty-Fifth International Joint Conference on
               Artificial Intelligence, {IJCAI-26}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Diego Calvanese},
  pages     = {7129--7137},
  year      = {2026},
  month     = {8},
  note      = {AI and Social Good},
  doi       = {10.24963/ijcai.2026/793},
  url       = {https://doi.org/10.24963/ijcai.2026/793},
}

TY  - CPAPER
TI  - CHAM-net: A Contrastive Hierarchical Adaptive Meta-network for Robust Global Methane Flux Prediction
AU  - Dong, Rongchao
AU  - Sun, Yiming
AU  - Chen, Shuo
AU  - Oh, Youmi
AU  - Liu, Licheng
AU  - Xie, Yiqun
AU  - Jia, Xiaowei
T2  - Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2026
SP  - 7129
EP  - 7137
DO  - 10.24963/ijcai.2026/793
UR  - https://doi.org/10.24963/ijcai.2026/793
ER  -