干旱区生态环境知识资源中心(Arid): Spatial prediction and modeling of soil salinity using simple cokriging, artificial neural networks, and support vector machines in El Outaya plain, Biskra, southeastern Algeria

Arid

	Spatial prediction and modeling of soil salinity using simple cokriging, artificial neural networks, and support vector machines in El Outaya plain, Biskra, southeastern Algeria
	Boudibi Samir; Sakaa Bachir; Benguega Zineeddine; Fadlaoui Haroun; Othman Tarek; Bouzidi Narimen
来源期刊	中国地球化学学报
ISSN	2096-0956
出版年	2021
卷号	40 期号:3 页码:2096-0956
英文摘要	Soil salinization is one of the most predominant environmental hazards responsible for agricultural land degradation, especially in the arid and semi-arid regions. An accurate spatial prediction and modeling of soil salinity in agricultural land are so important for farmers and decision- makers to develop the appropriate mechanisms to prevent the loss of fertile soil and increase crop production. El Outaya plain is marked by soil salinity increases due to the excessive use of poor groundwater quality for irrigation. This study aims to compare the performance of simple kriging, cokriging (SCOK), multilayer perceptron neural networks (MLP-NN), and support vector machines (SVM) in the prediction of topsoil and subsoil salinity. The field covariates including geochemical properties of irrigation groundwater and physical properties of soil and environmental covariates including digital elevation model and remote sensing derivatives were used as input candidates to SCOK, MLP-NN, and SVM. The optimal input combination was determined using multiple linear stepwise regression (MLSR). The results revealed that the SCOK using field covariates including water electrical conductivity (ECw) and sand percentage (sand %), and environmental covariates including land surface temperature (LST), topographic wetness index (TWI), and elevation could significantly increase the accuracy of soil salinity spatial prediction. The comparison of the prediction accuracy of the different modeling techniques using the Taylor diagram indicated that MLP-NN using LST, TWI, and elevation as inputs were more accurate in predicting the topsoil salinity [ECs (TS)] with a mean absolute error (MAE) of 0.43, root mean square error (RMSE) of 0.6 and correlation coefficient of 0.946. MLP-NN using ECw and sand % as inputs were more accurate in predicting the subsoil salinity [ECs (SS)] with MAE of 0.38, RMSE of 0.6, and R of 0.968.
英文关键词	Soil salinity Cokriging Multilayer perceptron Machine learning El-Outaya plain
类型	Article
语种	英语
国家	中国
收录类别	CSCD
WOS类目	Agriculture
CSCD记录号	CSCD:6980913
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/365417
作者单位	Boudibi Samir, Laboratory of Ecosystem Diversity and Agricultural Production System Dynamics in Arid Zones (DEDSPAZA), Department of Agricultural Sciences, Mohamed Khider University;;Scientific and Technical Research Center on Arid Regions (CRSTRA), Campus of Mohamed Khider University, ;;, Biskra;;Biskra, Algeria;;Algeria ;;07000.; Sakaa Bachir, Scientific and Technical Research Center on Arid Regions (CRSTRA), Campus of Mohamed Khider University, Biskra, 07000, Algeria.; Benguega Zineeddine, Scientific and Technical Research Center on Arid Regions (CRSTRA), Campus of Mohamed Khider University, Biskra, 07000, Algeria.; Fadlaoui Haroun, Scientific and Technical Research Center on Arid Regions (CRSTRA), Campus of Mohamed Khider University, Biskra, 07000, Algeria.; Othman Tarek, Scientific and Technical Research Center on Arid Regions (CRSTRA), Campus of Mohamed Khider University, Biskra, 07000, Algeria.; Bouzidi Narimen, Scientific and Technical Research Center on Arid Regions (CRSTRA), Campus of Mohamed Kh...
推荐引用方式 GB/T 7714	Boudibi Samir,Sakaa Bachir,Benguega Zineeddine,et al. Spatial prediction and modeling of soil salinity using simple cokriging, artificial neural networks, and support vector machines in El Outaya plain, Biskra, southeastern Algeria[J],2021,40(3):2096-0956.
APA	Boudibi Samir,Sakaa Bachir,Benguega Zineeddine,Fadlaoui Haroun,Othman Tarek,&Bouzidi Narimen.(2021).Spatial prediction and modeling of soil salinity using simple cokriging, artificial neural networks, and support vector machines in El Outaya plain, Biskra, southeastern Algeria.中国地球化学学报,40(3),2096-0956.
MLA	Boudibi Samir,et al."Spatial prediction and modeling of soil salinity using simple cokriging, artificial neural networks, and support vector machines in El Outaya plain, Biskra, southeastern Algeria".中国地球化学学报 40.3(2021):2096-0956.