干旱区生态环境知识资源中心(Arid): Comparison of Stepwise Multilinear Regressions, Artificial Neural Network, and Genetic Algorithm-Based Neural Network for Prediction the Plant Available Water of Unsaturated Soils in a Semi-arid Region of Iran (Case Study: Chaharmahal Bakhtiari Province)

Arid

DOI	10.1080/00103624.2020.1822385
	Comparison of Stepwise Multilinear Regressions, Artificial Neural Network, and Genetic Algorithm-Based Neural Network for Prediction the Plant Available Water of Unsaturated Soils in a Semi-arid Region of Iran (Case Study: Chaharmahal Bakhtiari Province)
	Soleimani, Reihaneh; Chavoshi, Elham; Shirani, Hossein; Esfandiar Pour, Isa
通讯作者	Chavoshi, E
来源期刊	COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS
ISSN	0010-3624
EISSN	1532-2416
出版年	2020
卷号	51 期号:17 页码:2297-2309
英文摘要	Plant available water (PAW) is one of the physical parameters of soils and the basic data of irrigation plans. Although various theoretical or empirical approaches have been proposed to describe this phenomenon, it is still possible to investigate and evaluate the relevance and applicability of new sciences such as artificial neural network method in predicting this phenomenon. In existing methods for determination of PAW, time-consuming tests are required. Nowadays, the capabilities of artificial neural network (ANN) methods in modeling have led to the use of ANN in parallel with the application of conventional approaches in various engineering sciences. In this study, artificial neural networks have been used as a new method to predict the PAW of soils. The study area is Khanimirza plain in Chaharmahal va Bakhtiari province. Soil sampling was performed randomly from 0 to 20 cm depth. The measured property in this study was the amount of plant available water (PAW). Readily available parameters including sand, silt and clay percentage, organic carbon, bulk density (BD), pH, Electrical conductivity (EC), calcium carbonate equivalent (CCE), and calcium carbonate are considered as model inputs. Modeling was performed using Stepwise multilinear regressions (SMLR), artificial neural network (ANN) and genetic algorithm-based neural network (ANN-GA). The results of PAW modeling showed that ANN-GA model with 0.90 coefficient is better than the other two methods. In general, ANN and ANN-GA showed better performance than SMLR. In fact, ANN and ANN-GA do not use a special type of equations and the network can achieve satisfactory results by establishing a proper relationship between input and output data.
英文关键词	Available water soil neural network genetic algorithm
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000575236400001
WOS关键词	PEDOTRANSFER FUNCTIONS ; MULTIPLE-REGRESSION ; RETENTION ; MODEL ; SYSTEM ; CARBON ; YIELD
WOS类目	Agronomy ; Plant Sciences ; Chemistry, Analytical ; Soil Science
WOS研究方向	Agriculture ; Plant Sciences ; Chemistry
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/326637
作者单位	[Soleimani, Reihaneh; Chavoshi, Elham] Islamic Azad Univ, Dept Soil Sci, Coll Agr, Isfahan Khorasgan Branch, Esfahan, Iran; [Shirani, Hossein; Esfandiar Pour, Isa] Vali E Asr Univ Rafsanjan, Dept Soil Sci, Coll Agr, Kerman, Iran
推荐引用方式 GB/T 7714	Soleimani, Reihaneh,Chavoshi, Elham,Shirani, Hossein,et al. Comparison of Stepwise Multilinear Regressions, Artificial Neural Network, and Genetic Algorithm-Based Neural Network for Prediction the Plant Available Water of Unsaturated Soils in a Semi-arid Region of Iran (Case Study: Chaharmahal Bakhtiari Province)[J],2020,51(17):2297-2309.
APA	Soleimani, Reihaneh,Chavoshi, Elham,Shirani, Hossein,&Esfandiar Pour, Isa.(2020).Comparison of Stepwise Multilinear Regressions, Artificial Neural Network, and Genetic Algorithm-Based Neural Network for Prediction the Plant Available Water of Unsaturated Soils in a Semi-arid Region of Iran (Case Study: Chaharmahal Bakhtiari Province).COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS,51(17),2297-2309.
MLA	Soleimani, Reihaneh,et al."Comparison of Stepwise Multilinear Regressions, Artificial Neural Network, and Genetic Algorithm-Based Neural Network for Prediction the Plant Available Water of Unsaturated Soils in a Semi-arid Region of Iran (Case Study: Chaharmahal Bakhtiari Province)".COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS 51.17(2020):2297-2309.