干旱区生态环境知识资源中心(Arid): Optimal feature selection for prediction of wind erosion threshold friction velocity using a modified evolution algorithm

Arid

DOI	10.1016/j.geoderma.2019.07.031
	Optimal feature selection for prediction of wind erosion threshold friction velocity using a modified evolution algorithm
	Kouchami-Sardoo, I 1; Shirani, H.2; Esfandiarpour-Boroujeni, I 2; Alvaro-Fuentes, J.3; Shekofteh, H.4
通讯作者	Shirani, H.
来源期刊	GEODERMA
ISSN	0016-7061
EISSN	1872-6259
出版年	2019
卷号	354
英文摘要	Threshold friction velocity (u(t)) is a very important parameter, which represents wind erosion potential. Because of the difficulty of measuring u(t), it would be advantageous if u(t) could be estimated indirectly from its effecting factors that can be easily measured. The main purpose of this research was to quantify relationships between u(t) and various topsoil features using inexpensive approaches. To prepare a reliable dataset, we used a portable wind tunnel for measuring u(t) at a total of 118 observation points in Kerman province, southeast Iran. We developed a non-dominated sorting genetic algorithm II (NSGA-II), specifically designed to operate with artificial neural network (ANN) to select the most determinant properties that influence u(t). A permutation of nine input features including surface crust (SC), gravel coverage (GC), very fine sand (VFS), fine sand (FS), very coarse sand (VCS), electrical conductivity (EC), sodium adsorption ratio (SAR), calcium carbonate equivalent (CCE), and mean weight diameter (MWD), was introduced as explanatory variables. We also examined the potential power of using a Multi-Layer Perception (MLP) neural network for prediction of u(t) changes in response to spatial variation of the selected features. The results of constructed MLP model revealed the ability of the model for u(t) prediction and showed that the coefficient of determination (R-2) values were 0.91 and 0.89 for training and testing data, respectively. Furthermore, acceptable level of the statistical validation criteria verified reliable performance of the MLP model. This research provided a powerful basis for prediction of u(*t) from topsoil features and surface roughness in arid and semi-arid areas of Iran; however, its generic framework could be used to other arid and semi-arid regions with similar challenges.
英文关键词	Wind erosion Wind tunnel Optimization Artificial neural network Genetic algorithm
类型	Article
语种	英语
国家	Iran ; Spain
收录类别	SCI-E
WOS记录号	WOS:000486133300031
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; GENETIC ALGORITHM ; SOIL-EROSION ; TUNNEL SIMULATION ; FARS PROVINCE ; TRANSPORT ; SUSCEPTIBILITY ; ROUGHNESS ; SURFACES ; RAINFALL
WOS类目	Soil Science
WOS研究方向	Agriculture
EI主题词	2019-11-15
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/310890
作者单位	1.Vali E Asr Univ Rafsanjan, Coll Agr, Dept Soil Sci, Rafsanjan, Iran; 2.Vali E Asr Univ Rafsanjan, Fac Agr, Dept Soil Sci, Rafsanjan 7718897111, Iran; 3.CSIC, Spanish Natl Res Council, EEAD, Zaragoza, Spain; 4.Univ Jiroft, Fac Agr, Dept Soil Sci, Jiroft, Iran
推荐引用方式 GB/T 7714	Kouchami-Sardoo, I,Shirani, H.,Esfandiarpour-Boroujeni, I,et al. Optimal feature selection for prediction of wind erosion threshold friction velocity using a modified evolution algorithm[J],2019,354.
APA	Kouchami-Sardoo, I,Shirani, H.,Esfandiarpour-Boroujeni, I,Alvaro-Fuentes, J.,&Shekofteh, H..(2019).Optimal feature selection for prediction of wind erosion threshold friction velocity using a modified evolution algorithm.GEODERMA,354.
MLA	Kouchami-Sardoo, I,et al."Optimal feature selection for prediction of wind erosion threshold friction velocity using a modified evolution algorithm".GEODERMA 354(2019).