Spatial Prediction of Soil Microplastics and Organic Matter Using Graph Attention Networks
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 a...
| Authors | Anik Dev Nath, Md Al Amin, Bikash Kumar Paul |
|---|---|
| Primary category | cs.LG |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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