CHAM-net通过动态建模站点特异性，提升甲烷通量预测精度
CHAM-net: A Contrastive Hierarchical Adaptive Meta-network for Robust Global Methane Flux Prediction
- 分层编码器-解码器架构，动态融合历史数据与站点特征
- 在模拟与观测数据上实现最低0.43的nRMSE和最高0.97的R2
- 适合需要高精度长期甲烷通量预测的研究者
甲烷是强效温室气体，显著加剧全球变暖。然而，由于环境驱动因子间复杂交互且时空异质性显著，准确估算全球甲烷排放与消耗仍具挑战。现有数据驱动方法常忽略生态系统的固有时空异质性，未能显式捕捉站点特性和跨年演化动态。为此，我们提出对比式分层自适应元网络（CHAM-net），显式从历史上下文中学习以捕获站点特异性动态。CHAM-net采用分层编码器-解码器架构，编码器从历史数据中提取站点特征，并动态调节解码器生成最终预测。实验表明，该模型在模拟与观测数据集上均优于所有基线方法，甲烷排放预测的nRMSE低至0.43，R²最高达0.97；消耗预测的nRMSE为0.88，R²最高达0.68。
原文摘要 · Abstract (English)
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 R2 scores up to 0.97 and 0.68 for emission prediction.
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