Articles | Volume 16, issue 10
https://doi.org/10.5194/gmd-16-2777-2023
https://doi.org/10.5194/gmd-16-2777-2023
Development and technical paper
 | 
24 May 2023
Development and technical paper |  | 24 May 2023

A generalized spatial autoregressive neural network method for three-dimensional spatial interpolation

Junda Zhan, Sensen Wu, Jin Qi, Jindi Zeng, Mengjiao Qin, Yuanyuan Wang, and Zhenhong Du

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Cited articles

Abd El-Hady, A. E.-N. M., Abdelaty, E. F., and Salama, A. E.: GIS-mapping of soil available plant nutrients (potentiality, gradient, anisotropy), OJSS, 8, 315–329, https://doi.org/10.4236/ojss.2018.812023, 2018. 
Adhikary, S. K., Muttil, N., and Yilmaz, A. G.: Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments, Hydrol. Process., 31, 2143–2161, https://doi.org/10.1002/hyp.11163, 2017. 
Allard, D., Senoussi, R., and Porcu, E.: Anisotropy models for spatial data, Math. Geosci., 48, 305–328, https://doi.org/10.1007/s11004-015-9594-x, 2016. 
Arowolo, A. O., Bhowmik, A. K., Qi, W., and Deng, X.: Comparison of spatial interpolation techniques to generate high-resolution climate surfaces for Nigeria, Int. J. Climatol, 37, 179–192, https://doi.org/10.1002/joc.4990, 2017. 
Aumond, P., Can, A., Mallet, V., De Coensel, B., Ribeiro, C., Botteldooren, D., and Lavandier, C.: Kriging-based spatial interpolation from measurements for sound level mapping in urban areas, J. Acoust. Soc. Am., 143, 2847–2857, https://doi.org/10.1121/1.5034799, 2018. 
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Short summary
We develop a generalized spatial autoregressive neural network model used for three-dimensional spatial interpolation. Taking the different changing trend of geographic elements along various directions into consideration, the model defines spatial distance in a generalized way and integrates it into the process of spatial interpolation with the theories of spatial autoregression and neural network. Compared with traditional methods, the model achieves better performance and is more adaptable.
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