ConvFormer-KDE: A Long-Term Point–Interval Prediction Framework for PM_2.5 Based on Multi-Source Spatial and Temporal Data

Accurate long-term PM_2.5 prediction is crucial for environmental management and public health. However, previous studies have mainly focused on short-term air quality point predictions, neglecting the importance of accurately predicting the long-term trends of PM_2.5 and studyin...

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Detalhes bibliográficos

Principais autores:	Shaofu Lin, Yuying Zhang, Xingjia Fei, Xiliang Liu, Qiang Mei
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2024-07-01
coleção:	Toxics
Assuntos:	fine particulate matter long-term point prediction interval prediction convolutional neural network transformer kernel density estimation
Acesso em linha:	https://www.mdpi.com/2305-6304/12/8/554
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