干旱区生态环境知识资源中心(Arid): A soft-computing approach to estimate soil electrical conductivity

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DOI	10.1016/j.biosystemseng.2021.02.015
	A soft-computing approach to estimate soil electrical conductivity
	Motie, Jalal Baradaran; Aghkhani, Mohammad H.; Rohani, Abbas; Lakzian, Amir
通讯作者	Motie, JB ; Aghkhani, MH (corresponding author), Ferdowsi Univ Mashhad, Dept Biosyst Engn, Mashhad, Razavi Khorasan, Iran.
来源期刊	BIOSYSTEMS ENGINEERING
ISSN	1537-5110
EISSN	1537-5129
出版年	2021
卷号	205 页码:105-120
英文摘要	Soil apparent electrical conductivity (ECa) is an indirect and rapid measurement for soil salinity, but because of its dependency on some physical and chemical properties of soil in addition to salinity, consideration of the soil extract EC is preferred for monitoring soil salinity, especially in semi-arid areas, though its measurement needs laboratory processes. This study, therefore, sought to develop a multivariable model to estimate the soil ECe from soil ECa, temperature, moisture content, bulk density, and clay percentage, using radial basis function (RBF) artificial neural network (ANN). In the first step, a set of tests was performed in laboratory in Box-Behnken design (BBD) to train the RBF-ANN. The developed RBF estimated the soil ECe with R-2 = 0.99 and RMSE = 0.005 dS.m(-1). Moreover, a quadratic response surface model (RSM) was also developed to compare with the RBF model. The sensitivity analysis revealed that ECa, moisture, bulk density, and temperature had the maximum to minimum effect on the estimation of soil ECe, respectively. In the second step, the RBF and RSM models were validated by another dataset obtained from three sites located in a semi-arid area. They were applied in-field with a multi-sensor portable device. The R-2 and RMSE of the estimation of ECe by the RBF were equal to 0.801 and 0.350 dS.m(-1), respectively. While, R-2 and RMSE of the RSM model were 0.735 and 0.439 dS.m(-1), respectively. The results of the study indicated excellent ability of the RBF-ANN in the rapid and precise estimation of soil ECe. (C) 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.
英文关键词	Electrical conductivity Artificial neural network Modelling Soil salinity Semi-arid area
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000640359200007
WOS关键词	NEURAL-NETWORKS ; SALINITY ; REGRESSION
WOS类目	Agricultural Engineering ; Agriculture, Multidisciplinary
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/2XILL650/349721
作者单位	[Motie, Jalal Baradaran; Aghkhani, Mohammad H.; Rohani, Abbas] Ferdowsi Univ Mashhad, Dept Biosyst Engn, Mashhad, Razavi Khorasan, Iran; [Lakzian, Amir] Ferdowsi Univ Mashhad, Dept Soil Sci, Mashhad, Razavi Khorasan, Iran
推荐引用方式 GB/T 7714	Motie, Jalal Baradaran,Aghkhani, Mohammad H.,Rohani, Abbas,et al. A soft-computing approach to estimate soil electrical conductivity[J],2021,205:105-120.
APA	Motie, Jalal Baradaran,Aghkhani, Mohammad H.,Rohani, Abbas,&Lakzian, Amir.(2021).A soft-computing approach to estimate soil electrical conductivity.BIOSYSTEMS ENGINEERING,205,105-120.
MLA	Motie, Jalal Baradaran,et al."A soft-computing approach to estimate soil electrical conductivity".BIOSYSTEMS ENGINEERING 205(2021):105-120.