Spatial Prediction of Soil Microplastics and Organic Matter Using Graph Attention Networks
Summary
This paper presents a Graph Attention Network (GAT) approach to model spatial dependencies in soil samples for predicting microplastics and organic matter, achieving high R² values but limited cross-validation generalization due to small sample size.
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# Spatial Prediction of Soil Microplastics and Organic Matter Using Graph Attention Networks Source: [https://arxiv.org/abs/2607.22875](https://arxiv.org/abs/2607.22875) [View PDF](https://arxiv.org/pdf/2607.22875) > Abstract: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\. ## Submission history From: Md Al Amin \[[view email](https://arxiv.org/show-email/41817b8a/2607.22875)\] **\[v1\]**Fri, 24 Jul 2026 19:35:29 UTC \(941 KB\)
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