Machine learning facilitates connections between soil thermal conductivity, soil water content, and soil matric potential

Xiangwei Wang, Yanchen Gao, Jiagui Hou, Jiahui Yang, Kathleen Smits, Hailong He*

Journal of Hydrology, 633: 130950 (2024)

DOI ↗

论文解读

本研究利用机器学习方法建立了土壤热导率、含水量和基质势之间的定量关联。

研究要点

✓机器学习建立土壤热-水参数的定量关联
✓模型有效揭示了热-水耦合的非线性关系
✓预测精度优于传统经验模型
✓为土壤物理参数预测提供新技术手段

引用格式

APA

Xiangwei Wang, Yanchen Gao, Jiagui Hou, Jiahui Yang, Kathleen Smits, Hailong He (2024). Machine learning facilitates connections between soil thermal conductivity, soil water content, and soil matric potential. Journal of Hydrology, 633, 130950. https://doi.org/10.1016/j.jhydrol.2024.130950

BibTeX

@article{Wang2024,
  author    = {Xiangwei Wang, Yanchen Gao, Jiagui Hou, Jiahui Yang, Kathleen Smits, Hailong He},
  title     = {Machine learning facilitates connections between soil thermal conductivity, soil water content, and soil matric potential},
  journal   = {Journal of Hydrology},
  year      = {2024},
  volume    = {633},
  pages     = {130950},
  doi       = {10.1016/j.jhydrol.2024.130950},
}