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DOI	10.1016/j.still.2018.12.023
	A reliable linear stochastic daily soil temperature forecast model
	Zeynoddin, Mohammad 1; Bonakdari, Hossein 1,2; Ebtehaj, Isa 1,2; Esmaeilbeiki, Fatemeh 3; Gharabaghi, Bahram 4; Haghi, Davoud Zare 3
通讯作者	Bonakdari, Hossein
来源期刊	SOIL & TILLAGE RESEARCH
ISSN	0167-1987
EISSN	1879-3444
出版年	2019
卷号	189 页码:73-87
英文摘要	Forecasting soil temperature profile is recognized as vital information for irrigation demand forecast in a modem/efficient agricultural water management framework in arid regions. A new linear stochastic model is proposed to more accurately forecast daily soil temperature (DST) profile at depths of 5, 10 and 20 cm below ground surface. The data used to test the proposed new method is collected from two stations in Tabriz and Jolfa, located in the East Azerbaijan Province of Iran. The proposed new method uses four preprocessing techniques, including spectral analysis, standardization, trend removing and differencing. A total of 1680 different modelling scenarios were performed in this study. The results show the superior ability of the proposed methodology in DST estimation, compared to existing nonlinear methods such as the multilayer perceptron neural network (MLPNN), with excellent performance indicators such as the coefficient of determination, mean relative error and the Nash-Sutcliffe index. Moreover, the Akaike Information Criterion (AICc) index is employed to compare the performance of the proposed method with MLPNN in terms of both accuracy and easy-of-use. The AICc of the proposed method at Jolfa at a depth of 5, 10 and 20 cm were 176, -2 and -184, respectively, in comparison with 1991, 30 and -57 for MLPNN. Similarly, the AICc index for Tabriz at 5, 10 and 20 cm are 200, 17 and -152, respectively, for the proposed method and 202, 33 and -62 for MLPNN. Consequently, the proposed new linear method is recommended for forecasting daily soil temperature profiles.
英文关键词	Daily soil temperature Stochastic based methodology Pre-processing Standardization Spectral analysis
类型	Article
语种	英语
国家	Iran ; Canada
收录类别	SCI-E
WOS记录号	WOS:000461407400008
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; EXTREME LEARNING-MACHINE ; SEDIMENT TRANSPORT ; DETECT TREND ; ALGORITHM ; PREDICTION ; BROMIDE ; 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/2XILL650/218867
作者单位	1.Razi Univ, Dept Civil Engn, Kermanshah, Iran; 2.Razi Univ, Environm Res Ctr, Kermanshah, Iran; 3.Univ Tabriz, Coll Agr, Dept Agr Soil Sci, Tabriz, Iran; 4.Univ Guelph, Sch Engn, Guelph, ON N1G 2W1, Canada
推荐引用方式 GB/T 7714	Zeynoddin, Mohammad,Bonakdari, Hossein,Ebtehaj, Isa,et al. A reliable linear stochastic daily soil temperature forecast model[J],2019,189:73-87.
APA	Zeynoddin, Mohammad,Bonakdari, Hossein,Ebtehaj, Isa,Esmaeilbeiki, Fatemeh,Gharabaghi, Bahram,&Haghi, Davoud Zare.(2019).A reliable linear stochastic daily soil temperature forecast model.SOIL & TILLAGE RESEARCH,189,73-87.
MLA	Zeynoddin, Mohammad,et al."A reliable linear stochastic daily soil temperature forecast model".SOIL & TILLAGE RESEARCH 189(2019):73-87.