干旱区生态环境知识资源中心(Arid): Land suitability modeling by parametric-based neural networks and fuzzy methods for soybean production in a semi-arid region

Arid

DOI	10.1007/s40808-016-0152-4
	Land suitability modeling by parametric-based neural networks and fuzzy methods for soybean production in a semi-arid region
	Bagherzadeh, Ali; Ghadiri, Elnaz; Darban, Ali Reza Souhani; Gholizadeh, Amin
通讯作者	Bagherzadeh, A
来源期刊	MODELING EARTH SYSTEMS AND ENVIRONMENT
ISSN	2363-6203
EISSN	2363-6211
出版年	2016
卷号	2 期号:2
英文摘要	Land evaluation is the process of predicting land use potential on the basis of its attributes. In the present study, the qualitative land suitability evaluation using parametric learning neural networks and fuzzy models was investigated for irrigated soybean production based on FAO land evaluation frameworks (FAO 1976, 1983, 1985) and the proposed methods by Sys et al. (1991c) and Hwang and Yoon (1981) in Neyshabour plain, Northeast of Iran. Some 41 land units were studied at the study area by a precise soil survey and their morphological and physicochemical properties. The Climatic and land qualities/characteristics for soybean crop were determined using the tables of soil and crop requirements developed by Sys et al. (1993). An interpolation function in GIS was used to map values to scores in terms of land qualities/characteristics for the land utilization type. Our results indicated that the most limiting factor for soybean cultivation in the study area was soil fertility properties. The values of land indexes by neural networks model ranged from 29.77 in some parts in west and middle to 57.45 in the north west and east parts of the study area, which categorized the plain from marginally suitable (S3) to moderately suitable (S2) classes. The land index values by fuzzy model varied between 16.10 and 47.80 which classified from marginally not suitable (N1) to marginally suitable (S3) classes. The coefficient of determination between the neural network land index values and the corresponding fuzzy values revealed a high correlation (R-2 = 0.966) between two models. The exponential regression coefficient (R-2) between the land indexes of neural networks and fuzzy models with the observed soybean yield in the study area varied between 0.610 and 0.514 respectively, which revealed higher performance of neural networks in predicting land suitability for irrigated soybean production in the study area.
英文关键词	Land evaluation Suitability Neural networks Fuzzy logic Soybean Yield
类型	Article
语种	英语
开放获取类型	Bronze
收录类别	ESCI
WOS记录号	WOS:000443087600055
WOS关键词	MAPS
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/332000
作者单位	[Bagherzadeh, Ali; Ghadiri, Elnaz; Darban, Ali Reza Souhani; Gholizadeh, Amin] Islamic Azad Univ, Dept Agr, Mashhad Branch, Emamyeh Blvd,POB 91735-413, Mashhad, Iran
推荐引用方式 GB/T 7714	Bagherzadeh, Ali,Ghadiri, Elnaz,Darban, Ali Reza Souhani,et al. Land suitability modeling by parametric-based neural networks and fuzzy methods for soybean production in a semi-arid region[J],2016,2(2).
APA	Bagherzadeh, Ali,Ghadiri, Elnaz,Darban, Ali Reza Souhani,&Gholizadeh, Amin.(2016).Land suitability modeling by parametric-based neural networks and fuzzy methods for soybean production in a semi-arid region.MODELING EARTH SYSTEMS AND ENVIRONMENT,2(2).
MLA	Bagherzadeh, Ali,et al."Land suitability modeling by parametric-based neural networks and fuzzy methods for soybean production in a semi-arid region".MODELING EARTH SYSTEMS AND ENVIRONMENT 2.2(2016).