干旱区生态环境知识资源中心(Arid): Spatial modelling of soil salinity: deep or shallow learning models?

Arid

DOI	10.1007/s11356-021-13503-7
	Spatial modelling of soil salinity: deep or shallow learning models?
	Mohammadifar, Aliakbar; Gholami, Hamid; Golzari, Shahram; Collins, Adrian L.
通讯作者	Gholami, H (corresponding author), Univ Hormozgan, Dept Nat Resources Engn, Bandar Abbas, Hormozgan, Iran.
来源期刊	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
ISSN	0944-1344
EISSN	1614-7499
出版年	2021
英文摘要	Understanding the spatial distribution of soil salinity is required to conserve land against degradation and desertification. Against this background, this study is the first attempt to predict soil salinity in the Jaghin basin, in southern Iran, by applying and comparing the performance of four deep learning (DL) models (deep convolutional neural networks-DCNNs, dense connected deep neural networks-DenseDNNs, recurrent neural networks-long short-term memory-RNN-LSTM and recurrent neural networks-gated recurrent unit-RNN-GRU) and six shallow machine learning (ML) models (bagged classification and regression tree-BCART, cforest, cubist, quantile regression with LASSO penalty-QR-LASSO, ridge regression-RR and support vectore machine-SVM). To do this, 49 environmental landsat8-derived variables including digital elevation model (DEM)-extracted covariates, soil-salinity indices, and other variables (e.g., soil order, lithology, land use) were mapped spatially. For assessing the relationships between soil salinity (EC) and factors controlling EC, we collected 319 surficial (0-5 cm depth) soil samples for measuring soil salinity on the basis of electrical conductivity (EC). We then selected the most important features (covariates) controlling soil salinity by applying a MARS model. The performance of the DL and shallow ML models for generating soil salinity spatial maps (SSSMs) was assessed using a Taylor diagram and the Nash Sutcliff coefficient (NSE). Among all 10 predictive models, DL models with NSE >= 0.9 (DCNNs was the most accurate model with NSE = 0.96) were selected as the four best models, and performed better than the six shallow ML models with NSE <= 0.83 (QR-LASSO was the weakest predictive model with NSE = 0.50). Based on DCNNs-, the values of the EC ranged between 0.67 and 14.73 dS/m, whereas for QR-LASSO the corresponding EC values were 0.37 to 19.6 dS/m. Overall, DL models performed better than shallow ML models for production of the SSSMs and therefore we recommend applying DL models for prediction purposes in environmental sciences.
英文关键词	Deep learning models Shallow machine learning models Soil salinity spatial maps Deep convolutional neural networks
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000631811800004
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/352178
作者单位	[Mohammadifar, Aliakbar; Gholami, Hamid] Univ Hormozgan, Dept Nat Resources Engn, Bandar Abbas, Hormozgan, Iran; [Golzari, Shahram] Univ Hormozgan, Dept Elect & Comp Engn, Bandar Abbas, Hormozgan, Iran; [Golzari, Shahram] Univ Hormozgan, Deep Learning Res Grp, Bandar Abbas, Hormozgan, Iran; [Collins, Adrian L.] Rothamsted Res, Sustainable Agr Sci Dept, Okehampton EX20 2SB, Devon, England
推荐引用方式 GB/T 7714	Mohammadifar, Aliakbar,Gholami, Hamid,Golzari, Shahram,et al. Spatial modelling of soil salinity: deep or shallow learning models?[J],2021.
APA	Mohammadifar, Aliakbar,Gholami, Hamid,Golzari, Shahram,&Collins, Adrian L..(2021).Spatial modelling of soil salinity: deep or shallow learning models?.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH.
MLA	Mohammadifar, Aliakbar,et al."Spatial modelling of soil salinity: deep or shallow learning models?".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2021).