# Spatial Prediction of Soil Microplastics and Organic Matter Using Graph Attention Networks

arXiv: https://arxiv.org/abs/2607.22875 · HTML: https://mlsanity.com/paper/2607.22875/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 24 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran](https://mlsanity.com/paper/2007.12475/) (2020)
- [SSL-SoilNet: A Hybrid Transformer-based Framework with Self-Supervised Learning for Large-scale Soil Organic Carbon Prediction](https://mlsanity.com/paper/2308.03586/) (2023)
- [Biogeochemistry-Informed Neural Network (BINN) for Improving Accuracy of Model Prediction and Scientific Understanding of Soil Organic Carbon](https://mlsanity.com/paper/2502.00672/) (2025)
- [Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale](https://mlsanity.com/paper/1502.02513/) (2015)

## Abstract (arXiv)

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 (R2 = 0.87) for microplastics and 0.43 (R2 = 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.