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Forecasting PM2.5 using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability
Research article (Environmental Pollution, 2021) · cited 56× · AI/ML
Forecasting PM2.5 using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability
Summary
Forecasting PM2.5 using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability is a scholarly article[1].
Key Facts
Forecasting PM2.5 using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability's instance of is recorded as scholarly article[2].
References
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APA4ort.xyz Knowledge Graph. (2026). Forecasting PM2.5 using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability. Retrieved May 24, 2026, from https://4ort.xyz/entity/forecasting-pm2-5-using-hybrid-graph-convolution-based-model-considering-dynamic-wind-field-to-offer-the-benefit-of-spat
MLA“Forecasting PM2.5 using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/forecasting-pm2-5-using-hybrid-graph-convolution-based-model-considering-dynamic-wind-field-to-offer-the-benefit-of-spat.
BibTeX@misc{4ortxyz_forecasting-pm2-5-using-hybrid-graph-convolution-based-model-considering-dynamic-wind-field-to-offer-the-benefit-of-spat_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Forecasting PM2.5 using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability}}, year = {2026}, url = {https://4ort.xyz/entity/forecasting-pm2-5-using-hybrid-graph-convolution-based-model-considering-dynamic-wind-field-to-offer-the-benefit-of-spat}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Forecasting PM2.5 using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability — https://4ort.xyz/entity/forecasting-pm2-5-using-hybrid-graph-convolution-based-model-considering-dynamic-wind-field-to-offer-the-benefit-of-spat (retrieved 2026-05-24)
