# Enhancing Graph Neural Networks Using Proximity Graphs for Dust Source Emission Forecasting

Source: [Enhancing Graph Neural Networks Using Proximity Graphs for Dust Source Emission Forecasting](<https://www.sota2.com/research/paper/6a34cd3b206542251d7c605d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-06-20T05:00:05.764Z

Authors: Maryam Sanisales, Zahed Rahmati, Ali Darvishi Boloorani, Ali Vefghi

Published: 2026-06-18T06:05:08.000Z

## Abstract

Accurate prediction of dust source emissions is critical for mitigating the significant environmental and health hazards posed by dust storms. Traditional forecasting methods often struggle to capture the complex spatiotemporal dynamics of these phenomena.   In this paper, we demonstrate that proximity graphs enable Graph Neural Networks (GNNs) to effectively model the intricate spatial and temporal relationships between data points. Specifically, we use proximity graphs--such as Delaunay triangulation, Gabriel graph, k-Nearest Neighbor graph, and Yao graph--as the input for GNNs (including GraphSAGE, Graph Convolutional Networks, and Graph Attention Networks) to perform message passing.   Our approach highlights the effectiveness of integrating proximity graphs with GNNs for robust and accurate dust source forecasting. To emphasize the importance of proximity graph representations, we compare our method against GNNs using random graphs for message passing. The results show that GNNs with proximity graphs significantly outperform those with random graphs and are also far superior to Long Short-Term Memory (LSTM) model in dust source emission forecasting.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Dust emission forecasting on Dust emission forecasting dataset](<https://www.sota2.com/research/sota/dust-emission-forecasting-on-dust-emission-forecasting-dataset.md>): Dust emission forecasting; Dust emission forecasting dataset; AUC: 73.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2606.19825>)

[PDF](<https://arxiv.org/pdf/2606.19825.pdf>)