干旱区生态环境知识资源中心(Arid): Prediction of soil hydraulic properties by Gaussian process regression algorithm in arid and semiarid zones in Iran

Arid

DOI	10.1016/j.still.2021.104980
	Prediction of soil hydraulic properties by Gaussian process regression algorithm in arid and semiarid zones in Iran
	Rastgou, M.; Bayat, H.; Mansoorizadeh, M.; Gregory, Andrew S.
通讯作者	Bayat, H (corresponding author), Bu Ali Sina Univ, Fac Agr, Dept Soil Sci, Hamadan, Hamadan, Iran.
来源期刊	SOIL & TILLAGE RESEARCH
ISSN	0167-1987
EISSN	1879-3444
出版年	2021
卷号	210
英文摘要	The soil water retention curve (SWRC) is one of the principal soil hydraulic properties that is needed as input data in modeling water and solute transport through unsaturated soils. Field or laboratory measurement of SWRC is labor-intensive, expensive and time-consuming. Pedotransfer functions (PTFs) have been developed as an indirect method to predict soil hydraulic properties (e.g. SWRC) from more easily measured soil data by data mining tools. The novelty of the present study is the application of Gaussian process regression (GPR) algorithm as a data mining technique, to predict the SWRC, and comparing its performance with that of the multiple linear regression (MLR) and Rosetta methods, which has not been conducted so far. In this study 15 GPR and MLRbased PTFs were developed to predict the parameters of the van Genuchten model from different combinations of readily available properties of 223 soil samples that were taken from six provinces of Iran. The k-fold (k = 20) cross validation approach was utilized to obtain training and testing data sets for each PTF. The predictions of the GPR and MLR-based PTFs were evaluated by different criteria. The GPR-based PTFs had greater accuracy and reliability than MLR method in predicting SWRC according to integral root mean square error (IRMSE) criterion. However, the differences were not significant (P 0.05) in the testing step, but the reliability of both methods were significantly (P < 0.05) better than Rosetta-based PTFs. The covariance functions of GPR method can be effectively fitted by kernels with different features for modeling complex relationships. The GPR method can be considered as a competitive alternative to develop parametric-PTFs.
英文关键词	Data mining Gaussian process regression Hydraulic properties
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000641358800002
WOS关键词	WATER-RETENTION CURVE ; MULTIOBJECTIVE GROUP METHOD ; PARTICLE-SIZE DISTRIBUTION ; PEDOTRANSFER FUNCTIONS ; ORGANIC-MATTER ; PARAMETERS ; MODEL ; CONDUCTIVITY ; TEXTURE ; CARBON
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/2XILL650/368747
作者单位	[Rastgou, M.; Bayat, H.] Bu Ali Sina Univ, Fac Agr, Dept Soil Sci, Hamadan, Hamadan, Iran; [Mansoorizadeh, M.] Bu Ali Sina Univ, Fac Engn, Dept Comp Sci, Hamadan, Hamadan, Iran; [Gregory, Andrew S.] Rothamsted Res, Sustainable Agr Sci Dept, Harpenden AL5 2JQ, Herts, England
推荐引用方式 GB/T 7714	Rastgou, M.,Bayat, H.,Mansoorizadeh, M.,et al. Prediction of soil hydraulic properties by Gaussian process regression algorithm in arid and semiarid zones in Iran[J],2021,210.
APA	Rastgou, M.,Bayat, H.,Mansoorizadeh, M.,&Gregory, Andrew S..(2021).Prediction of soil hydraulic properties by Gaussian process regression algorithm in arid and semiarid zones in Iran.SOIL & TILLAGE RESEARCH,210.
MLA	Rastgou, M.,et al."Prediction of soil hydraulic properties by Gaussian process regression algorithm in arid and semiarid zones in Iran".SOIL & TILLAGE RESEARCH 210(2021).