气候变化领域集成服务门户(CCPortal): Multisite evaluation of physics-informed deep learning for permafrost prediction in the Qinghai-Tibet Plateau

CCPortal

DOI	10.1016/j.coldregions.2023.104009
	Multisite evaluation of physics-informed deep learning for permafrost prediction in the Qinghai-Tibet Plateau
	Liu, Yibo; Ran, Youhua; Li, Xin; Che, Tao; Wu, Tonghua
发表日期	2023
ISSN	0165-232X
EISSN	1872-7441
卷号	216
英文摘要	Prediction of the permafrost ground temperature is challenging due to its complex nonlinear process. Coupling physical models and machine learning has shown great potential in big data analysis, but its performance in permafrost prediction is still unclear. In this study, we proposed a physics-informed deep learning framework (i. e., PI-LSTM) to predict permafrost ground temperature profiles. The framework combines physical information with a long short-term memory (LSTM) network by two-stage training (pretraining and fine-tuning). The output of a Geophysical Institute Permafrost Laboratory 2 (GIPL2) model was used to pretrain the LSTM model. Borehole ground temperature measurements were used to fine-tune the pretrained LSTM model. Validation at multiple sites in various situations in the Qinghai-Tibet Plateau (QTP) shows that the accuracy and efficiency of the PI-LSTM model are dramatically higher than those of the original LSTM or GIPL2 model. Depending on the fine-tuning data sampling frequencies, the performance of the PI-LSTM model improved by an average of 27% (6-56%) and 69% (64-71%) compared to the LSTM and GIPL2 models, respectively. Even when only one year of observations was used to fine-tune the model, the RMSE of the simulated near-surface ground temperature profiles in the next decade did not substantially decline. The incorporation of physical information also contributes to the simulation efficiency. The PI-LSTM model converges faster than the LSTM model, and the training epochs are reduced by 70% (67-73%) during the fine-tuning step. This study demonstrates that integrating physical information into deep learning is promising for improving permafrost predictions.
关键词	Frozen ground Ground temperature GIPL2 Machine learning
英文关键词	ACTIVE-LAYER THICKNESS; THERMAL STATE; DISCOVERY; RUNOFF; CHINA; SOILS; BASIN
WOS研究方向	Engineering, Environmental ; Engineering, Civil ; Geosciences, Multidisciplinary
WOS记录号	WOS:001076412000001
来源期刊	COLD REGIONS SCIENCE AND TECHNOLOGY
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283169
作者单位	Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; Institute of Tibetan Plateau Research, CAS
推荐引用方式 GB/T 7714	Liu, Yibo,Ran, Youhua,Li, Xin,et al. Multisite evaluation of physics-informed deep learning for permafrost prediction in the Qinghai-Tibet Plateau[J]. 中国科学院青藏高原研究所,2023,216.
APA	Liu, Yibo,Ran, Youhua,Li, Xin,Che, Tao,&Wu, Tonghua.(2023).Multisite evaluation of physics-informed deep learning for permafrost prediction in the Qinghai-Tibet Plateau.COLD REGIONS SCIENCE AND TECHNOLOGY,216.
MLA	Liu, Yibo,et al."Multisite evaluation of physics-informed deep learning for permafrost prediction in the Qinghai-Tibet Plateau".COLD REGIONS SCIENCE AND TECHNOLOGY 216(2023).