干旱区生态环境知识资源中心(Arid): Spatio Prediction of Soil Capability Modeled with Modified RVFL Using Aptenodytes Forsteri Optimization and Digital Soil Assessment Technique

Arid

DOI	10.3390/su142214996
	Spatio Prediction of Soil Capability Modeled with Modified RVFL Using Aptenodytes Forsteri Optimization and Digital Soil Assessment Technique
	Alnaimy, Manal A.; Shahin, Sahar A.; Afifi, Ahmed A.; Ewees, Ahmed A.; Junakova, Natalia; Balintova, Magdalena; Abd Elaziz, Mohamed
通讯作者	Alnaimy, MA
来源期刊	SUSTAINABILITY
EISSN	2071-1050
出版年	2022
卷号	14 期号:22
英文摘要	To meet the needs of Egypt's rising population, more land must be cultivated. Land evaluation is vital to achieving sustainable agricultural production. To determine the soil capability in the northeast Nile Delta region of Egypt, the present study introduces a new form of integration between the Agriculture Land Evaluation System (ALES Arid) model and the machine learning (ML) approach. The soil capability indicators required for the ALES Arid model were determined for the 47 collected soil profiles covering the study area. These indicators include soil pH, soil salinity, the sodium adsorption ratio (SAR), the exchangeable sodium percentage (ESP), the organic matter (OM) content, the calcium carbonate (CaCO3) content, the gypsum content, the clay percentage, and the slope. The ALES Arid model was run using these indicators, and soil capability indexes were obtained. Using GIS, these indexes helped to classify the study area into four capability classes, ranging from good to very poor soils. To predict the soil capability, three machine learning algorithms named traditional RVFL, sine cosine algorithm (SCA), and AFO were also applied to the same soil criteria. The developed ML method aims to enhance the prediction of soil capability. This method depends on improving the performance of Random Vector Functional Link (RVFL) using an optimization technique named Aptenodytes Forsteri Optimization (AFO). The operators of AFO were used to determine the best parameters of RVFL since traditional RVFL is sensitive to parameters. To assess the performance of the developed AFO-RVFL method, a set of real collected data was used. The experimental results illustrate the high efficacy of AFO-RVFL in the spatial prediction of soil capability. The correlations found in this study are critical for understanding the overall techniques for predicting soil capability.
英文关键词	machine learning Aptenodytes Forsteri Optimization ALES Arid software land capability prediction soil mapping land evaluation arid regions
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000887648800001
WOS关键词	MACHINE LEARNING-MODELS ; GIS APPLICATION ; ORGANIC-CARBON ; NILE DELTA ; SUSCEPTIBILITY ; INFORMATION ; SUITABILITY ; SALINITY ; SYSTEM
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394677
推荐引用方式 GB/T 7714	Alnaimy, Manal A.,Shahin, Sahar A.,Afifi, Ahmed A.,et al. Spatio Prediction of Soil Capability Modeled with Modified RVFL Using Aptenodytes Forsteri Optimization and Digital Soil Assessment Technique[J],2022,14(22).
APA	Alnaimy, Manal A..,Shahin, Sahar A..,Afifi, Ahmed A..,Ewees, Ahmed A..,Junakova, Natalia.,...&Abd Elaziz, Mohamed.(2022).Spatio Prediction of Soil Capability Modeled with Modified RVFL Using Aptenodytes Forsteri Optimization and Digital Soil Assessment Technique.SUSTAINABILITY,14(22).
MLA	Alnaimy, Manal A.,et al."Spatio Prediction of Soil Capability Modeled with Modified RVFL Using Aptenodytes Forsteri Optimization and Digital Soil Assessment Technique".SUSTAINABILITY 14.22(2022).