Physics-informed spatiotemporal graph neural network models for groundwater contaminant prediction
- DOI
- 10.1016/j.jconhyd.2026.105078
- Published
- 2026-11
- Container
- Journal of Contaminant Hydrology
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.jconhyd.2026.105078,
title = {Physics-informed spatiotemporal graph neural network models for groundwater contaminant prediction},
author = {Dai Wan and Ge Ying and Hu Danxin and Xu Ruibing and DengYi and Yi Shuping},
year = {2026},
journal = {Journal of Contaminant Hydrology},
doi = {10.1016/j.jconhyd.2026.105078},
url = {https://doi.org/10.1016/j.jconhyd.2026.105078}
}RIS
TY - JOUR TI - Physics-informed spatiotemporal graph neural network models for groundwater contaminant prediction AU - Dai Wan AU - Ge Ying AU - Hu Danxin AU - Xu Ruibing AU - DengYi AU - Yi Shuping PY - 2026 JO - Journal of Contaminant Hydrology DO - 10.1016/j.jconhyd.2026.105078 UR - https://doi.org/10.1016/j.jconhyd.2026.105078 ER -
APA
Wan, D., Ying, G., Danxin, H., Ruibing, X., DengYi, & Shuping, Y. (2026). Physics-informed spatiotemporal graph neural network models for groundwater contaminant prediction. Journal of Contaminant Hydrology. https://doi.org/10.1016/j.jconhyd.2026.105078
Source records
- crossref · retrieved 2026-09-25T11:42:57.959Z