AI 中文总结
该研究旨在用图注意力网络对土壤微塑料和有机质进行空间预测，通过纳入多种数据构建两层GAT架构，模型表现良好，但交叉验证显示泛化能力受限，证明了GAT潜力，强调需密集数据集和改进图连通性。
AI 中文摘要
准确估算土壤微塑料和有机质对于评估生态系统健康和支持可持续土地利用至关重要。本研究提出一种基于图的深度学习方法，使用图注意力网络（GAT）对91个地理参考土壤样本之间的空间依赖性进行建模。通过纳入空间坐标、土壤属性和土地利用数据，开发了一个两层GAT架构来捕捉局部相互作用。最终模型表现出色，微塑料的RMSE为625.06（$R^2 = 0.87$），有机质的RMSE为0.43（$R^2 = 0.91$）。然而，交叉验证结果显示泛化能力有限，可能是由于样本量小和图结构稀疏。这些发现证明了GAT在空间土壤预测中的潜力，并强调了密集数据集和改进图连通性的必要性。
英文摘要
Accurate estimation of soil microplastics and organic matter is essential to assess ecosystem health and support sustainable land use. This study presents a graph-based deep learning approach using Graph Attention Networks (GATs) to model spatial dependencies among 91 georeferenced soil samples. By incorporating spatial coordinates, soil properties, and land use data, a two-layer GAT architecture was developed to capture local interactions. The final model showed strong performance, achieving RMSEs of 625.06 ($R^2 = 0.87$) for microplastics and 0.43 ($R^2 = 0.91$) for organic matter. However, cross-validation results revealed limited generalization, probably due to the small sample size and sparse graph structure. These findings demonstrate the potential of GATs for spatial soil prediction and underscore the need for dense datasets and improved graph connectivity.
Comments11 Pages, 8 Figures
