2025
doi: 10.12720/jait.16.7.959-965
A Comparative Study of Neural Network Adaptations for Spatial Data
Bo-yu Chen1
,
Hao Zhang2,*
- 1Department of Statistics, Purdue University, West Lafayette, Indiana, USA
- 2Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, USA
- Manuscript receivedJanuary 21, 2025
- revisedFebruary 12, 2025
- acceptedMarch 10, 2025
- publishedJuly 15, 2025
Abstract
In this study, we evaluated the predictive performance of various deep neural network approaches for spatial data. Recent advances in neural networks have led to a surge in deep learning methods and applications. Many of these methods were developed implicitly for independent data, and it is not trivial to incorporate spatial correlation into them. However, several strategies have been proposed to adapt neural networks to account for spatial correlation. Using both simulated and real-world datasets, we compared spatial methods developed from fully connected deep neural networks, analyzing the impact of different spatial adaptations. Our findings aim to inform future research directions in this evolving field.
Keywords
- deep learning
- Kriging
- neural network
- prediction
- spatial statistics
How to Cite
Bo-yu Chen and Hao Zhang, "A Comparative Study of Neural Network Adaptations for Spatial Data," Journal of Advances in Information Technology, vol. 16, no. 7, pp. 959-965, 2025. https://doi.org/10.12720/jait.16.7.959-965
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
