arXiv Machine Learning By Maryam Sanisales, Zahed Rahmati, Ali Darvishi Boloorani, Ali Vefghi

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

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arXiv:2606. 19825v1 Announce Type: new Abstract: Accurate prediction of dust source emissions is critical for mitigating the significant environmental and health hazards posed by dust storms.

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arXiv AI
Sep 7

Predicting Spatiotemporal Mobile Sensing-Based PM2.5 Concentrations Using Low-Rank Adapted Spatially Attentive Graph Neural Network

This study presents a new mobile‑sensing dataset from Surat, India, capturing PM2.5 concentrations along with meteorological and land‑use variables. The authors model the data as a graph using two node‑definition strategies—uniform segmentation and DBSCAN clustering—and introduce a Spatially Attentive Graph Neural Network (SA‑GNN) that combines cluster‑specific GRUs with a Graph Attention Network to forecast fine‑grained, short‑term PM2.5 levels. SA‑GNN outperforms traditional LSTM, RNN, GRU, and ANN baselines, achieving an R² of 0.95, RMSE of 6.8, and MAE of 4.2 µg/m³ on the dataset.

By Om Chiddarwar, Priyanka Mandal, Praveen Kumar Chandaliya, Shriniwas Arkatkar