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Explainable spatiotemporal Graph Neural Networks: Multi-City PM2.5 forecasting in Bangladesh with geographic graph topology validation

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  • Pritom Ray Nobin

Abstract

Accurate forecasting of fine particulate matter (PM2.5) is critical for mitigating severe health risks in South Asia, yet it remains challenging in data-scarce regions like Bangladesh. While spatiotemporal graph neural networks (GNNs) offer a promising solution by capturing atmospheric transport, their effectiveness and the physical validity of their assumed spatial graphs have rarely been rigorously tested in such constrained settings. This study addresses this gap by evaluating a graph diffusion gated recurrent unit (GD-GRU) model using four years of daily, multi-source environmental data across a 12-city network. For forecasts up to three days ahead, the GD-GRU achieves an overall mean absolute error of 8.853 µg/m3 and an R2 of 0.666, significantly outperforming non-graph baselines under FDR-corrected Diebold–Mariano testing (p

Suggested Citation

  • Pritom Ray Nobin, 2026. "Explainable spatiotemporal Graph Neural Networks: Multi-City PM2.5 forecasting in Bangladesh with geographic graph topology validation," PLOS ONE, Public Library of Science, vol. 21(10), pages 1-36, October.
    • Handle: RePEc:plo:pone00:0351928
    DOI: 10.1371/journal.pone.0351928
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