干旱区生态环境知识资源中心(Arid): Modeling of soil exchangeable sodium percentage using easily obtained indices and artificial intelligence-based models

Arid

DOI	10.1007/s40808-016-0185-8
	Modeling of soil exchangeable sodium percentage using easily obtained indices and artificial intelligence-based models
	Keshavarzi, Ali; Bagherzadeh, Ali; Omran, El-Sayed Ewis; Iqbal, Munawar
通讯作者	Keshavarzi, 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:3
英文摘要	Salinization and alkalization of land resources are the major obstacles to their optimal usage in many arid and semi-arid regions of the world, including Iran, since potential evapotranspiration is more noteworthy than precipitation in these areas. The amount of water that enters the soil is low and this results in salt accumulation in soils, which makes the soil infertile. Moreover, existence of salts, for example, sodium, in soils causes dispersion of soil particles and soil degradation, and intensifies soil erosion too. Monitoring exchangeable sodium percentage (ESP) variability in soils is both time-consuming and costly. However, in order to estimate the amounts of amendments and land management, it is necessary to know ESP variation and values in sodic or saline and sodic soils. Thus, introducing a method, which utilizes easily obtained indices to estimate ESP indirectly is more optimized and economical. Input and output data, i.e., ECe (dS m(-1)), clay (%), pH and ESP (%) were collected and measured from 100 soil samples in light of a stratified random sampling from Mashhad Plain, Khorasan-e-Razavi Province, Northeast Iran. This study aims to propose some models to estimate ESP by easily obtained properties of soil. In this regard, the efficiency of artificial intelligence-based (AI) models (i.e., Artificial Neural Network, ANN, and Adaptive Neuro-Fuzzy Inference System, ANFIS) was investigated and compared. Accuracy results showed that owing to highest R-2 and the lowest mean square error (MSE), ANFIS model predictions were superior to the MLP model for indirect estimation of soil exchangeable sodium percentage.
英文关键词	Artificial intelligence Prediction Exchangeable sodium percentage Mashhad plain Iran
类型	Article
语种	英语
开放获取类型	Bronze
收录类别	ESCI
WOS记录号	WOS:000443617200023
WOS关键词	NEURAL-NETWORK ; WAVELET TRANSFORMS ; DRASTIC METHOD ; PLAIN AQUIFER ; FUZZY ; PREDICTION ; SALINITY ; ANFIS ; RISK
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/2XILL650/332006
作者单位	[Keshavarzi, Ali] Univ Tehran, Dept Soil Sci, Lab Remote Sensing & GIS, POB 4111, Karaj 3158777871, Iran; [Omran, El-Sayed Ewis] Suez Canal Univ, Fac Agr, Dept Soil & Water, Ismailia 41522, Egypt; [Bagherzadeh, Ali] Islamic Azad Univ, Dept Agr, Mashhad Branch, Emamyeh Blvd,POB 91735-413, Mashhad, Iran; [Iqbal, Munawar] Univ Lahore, Dept Chem, Raiwind Rd, Lahore, Pakistan; [Iqbal, Munawar] Qurtuba Univ Sci & Informat Technol, Dept Chem, Peshawar 25100, Kpk, Pakistan
推荐引用方式 GB/T 7714	Keshavarzi, Ali,Bagherzadeh, Ali,Omran, El-Sayed Ewis,et al. Modeling of soil exchangeable sodium percentage using easily obtained indices and artificial intelligence-based models[J],2016,2(3).
APA	Keshavarzi, Ali,Bagherzadeh, Ali,Omran, El-Sayed Ewis,&Iqbal, Munawar.(2016).Modeling of soil exchangeable sodium percentage using easily obtained indices and artificial intelligence-based models.MODELING EARTH SYSTEMS AND ENVIRONMENT,2(3).
MLA	Keshavarzi, Ali,et al."Modeling of soil exchangeable sodium percentage using easily obtained indices and artificial intelligence-based models".MODELING EARTH SYSTEMS AND ENVIRONMENT 2.3(2016).