干旱区生态环境知识资源中心(Arid): Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model

Arid

DOI	10.1007/s10462-020-09915-5
	Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model
	Alizamir, Meysam; Kim, Sungwon; Zounemat-Kermani, Mohammad; Heddam, Salim; Shahrabadi, Amin Hasanalipour; Gharabaghi, Bahram
通讯作者	Alizamir, M
来源期刊	ARTIFICIAL INTELLIGENCE REVIEW
ISSN	0269-2821
EISSN	1573-7462
英文摘要	Soil temperature (T-s) is an essential regulator of a plant's root growth, evapotranspiration rates, and hence soil water content. Over the last few years, in response to the climatic change, significant amount of research has been conducted worldwide to understand the quantitative link between soil temperature and the climatic factors, and it was highlighted that the hydrothermal conditions in the soil are continuously changing in response to the change of the hydro-meteorological factors. A large amount of the models have been developed and used in the past for the analysis and modelling of soil temperature, however, none of them has investigated the robustness and feasibilities of the deep echo state network (Deep ESN) model. A more accurate model for forecastingT(s)presents many worldwide opportunities in improving irrigation efficiency in arid climates and help attain sustainable water resources management. This research compares the application of the novel Deep ESN model versus three conventional machine learning models for soil temperature forecasting at 10 and 20 cm depths. We combined several critical daily hydro-meteorological data into six different input combinations for constructing the Deep ESN model. The accuracy of the developed soil temperature models is evaluated using three deterministic indices. The results of the evaluation indicate that the Deep ESN model outperformed conventional machine learning methods and can reduce the root mean square error (RMSE) accuracy of the traditional models between 30 and 60% in both stations. In the test phase, the most accurate estimation was obtained by Deep ESN at depths of 10 cm by RMSE = 2.41 degrees C and 20 cm by RMSE = 1.28 degrees C in Champaign station and RMSE = 2.17 degrees C (10 cm) and RMSE = 1.52 degrees C (20 cm) in Springfield station. The superior performance of the Deep ESN model confirmed that this model can be successfully applied for modellingT(s)based on meteorological paarameters.
英文关键词	Soil temperature Deep echo state network Multilayer perceptron neural network Random forest M5Prime tree
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000573787000002
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; EXTREME LEARNING-MACHINE ; REGRESSION ; PREDICTION ; CLIMATE ; GROUNDWATER ; TRANSPORT ; BROMIDE ; REGIONS
WOS类目	Computer Science, Artificial Intelligence
WOS研究方向	Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/328290
作者单位	[Alizamir, Meysam] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam; [Alizamir, Meysam] Duy Tan Univ, Fac Civil Engn, Da Nang 550000, Vietnam; [Kim, Sungwon] Dongyang Univ, Dept Railrd Construct & Safety Engn, Yeongju, South Korea; [Zounemat-Kermani, Mohammad] Shahid Bahonar Univ Kerman, Dept Water Engn, Kerman, Iran; [Heddam, Salim] Univ 20 Aout 1955, Lab Res Biodivers Interact Ecosyst & Biotechnol, Fac Sci, Agron Dept,Hydraul Div, Route El Hadaik,BP 26, Skikda, Algeria; [Shahrabadi, Amin Hasanalipour] Univ Sistan & Baluchestan, Dept Civil Engn, Zahedan, Iran; [Gharabaghi, Bahram] Univ Guelph, Sch Engn, Guelph, ON N1G 2W1, Canada
推荐引用方式 GB/T 7714	Alizamir, Meysam,Kim, Sungwon,Zounemat-Kermani, Mohammad,et al. Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model[J].
APA	Alizamir, Meysam,Kim, Sungwon,Zounemat-Kermani, Mohammad,Heddam, Salim,Shahrabadi, Amin Hasanalipour,&Gharabaghi, Bahram.
MLA	Alizamir, Meysam,et al."Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model".ARTIFICIAL INTELLIGENCE REVIEW