Spatiotemporal Interpolation Using Graph Neural Network

Author:

Yao Shiqi1ORCID,Huang Bo2ORCID

Affiliation:

1. Hunan Institute of Advanced Technology, China

2. Department of Geography and Resource Management, The Chinese University of Hong Kong, China

Publisher

Informa UK Limited

Subject

Earth-Surface Processes,Geography, Planning and Development

Reference77 articles.

1. Spatial estimation of urban air pollution with the use of artificial neural network models

2. Kriging Convolutional Networks

3. Arjovsky M. and L. Bottou. 2017. Towards principled methods for training generative adversarial networks. arXiv: 1701.04862 [cs stat]. Accessed April 15 2022. http://arxiv.org/abs/1701.04862.

4. Arjovsky M. S. Chintala and L. Bottou. 2017. Wasserstein generative adversarial networks. In Proceedings of the 34th International Conference on Machine Learning ed. D. Precup and Y. Whye Teh 214–23. PMLR. Accessed April 19 2022. https://proceedings.mlr.press/v70/arjovsky17a.html.

5. Botchkarev A. 2018. Performance metrics (error measures) in machine learning regression forecasting and prognostics: Properties and Typolog. Accessed August 13 2021. https://arxiv.org/ftp/arxiv/papers/1809/1809.03006.pdf.

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