干旱区生态环境知识资源中心(Arid): Soil salinity simulation based on electromagnetic induction and deep learning

Arid

DOI	10.1016/j.still.2023.105706
	Soil salinity simulation based on electromagnetic induction and deep learning
	Gu, Simin; Jiang, Simin; Li, Xianwen; Zheng, Na; Xia, Xuemin
通讯作者	Jiang, SM
来源期刊	SOIL & TILLAGE RESEARCH
ISSN	0167-1987
EISSN	1879-3444
出版年	2023
卷号	230
英文摘要	In pursuit of fine modeling and forecasting of water-salt transport in arid zones, this study uses the rapid electromagnetic induction survey as the main tool to establish an empirical relationship between soil volumetric water content, salinity and electrical conductivity based on on-site measurement data. By using deep learning to set up a surrogate model for electromagnetic induction inverse problem, the vertical distribution of soil salinity during salt leaching at non-growth season is revealed. HYDRUS-1D was used to perform inversions of soil hydraulic parameters to obtain accurate numerical solutions. The results show that the surrogate of electromagnetic induction inversion using a deep convolutional encoding-decoding network (NET) with a learning rate of 0.0006 and 1500 sets of training samples has high accuracy, with an R-2 of 0.9930 and RMSE of 0.2585 mg/cm(3). The HYDRUS inverse modeling was performed using synthetic ECa data, water content and soil salinity observation data, and the combination of ECa and shallow ground (10 cm deep) water content respectively. It was shown that although the accuracy of the water-salt transport model obtained by using ECa data is slightly lower than that of the inversion model using traditional observation data, it can still accurately simulate the water-salt distribution and has the incomparable advantages of being fast, convenient and low-cost, and the accuracy of the model is improved to some extent by incorporating shallow ground water content data into the inversion based on ECa data. Therefore, the water-salt transport simulation framework incorporating soil apparent electrical conductivity proposed in this study can accurately simulate salt transport and can provide strategies and methods for the simulation and prediction of soil water-salt transport using electromagnetic induction technology.
英文关键词	Electromagnetic induction Surrogate model Deep neural network Electromagnetic inversion HYDRUS inversion
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000971051700001
WOS关键词	ENCODER-DECODER NETWORKS ; ELECTRICAL-CONDUCTIVITY ; HYDRAULIC-PROPERTIES ; INVERSION ; WATER ; INSTRUMENT ; PREDICTION ; PROFILES ; SCALE ; FIELD
WOS类目	Soil Science
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398662
推荐引用方式 GB/T 7714	Gu, Simin,Jiang, Simin,Li, Xianwen,et al. Soil salinity simulation based on electromagnetic induction and deep learning[J],2023,230.
APA	Gu, Simin,Jiang, Simin,Li, Xianwen,Zheng, Na,&Xia, Xuemin.(2023).Soil salinity simulation based on electromagnetic induction and deep learning.SOIL & TILLAGE RESEARCH,230.
MLA	Gu, Simin,et al."Soil salinity simulation based on electromagnetic induction and deep learning".SOIL & TILLAGE RESEARCH 230(2023).