Spatial prediction and modeling of soil salinity using simple cokriging, artificial neural networks, and support vector machines in El Outaya plain, Biskra, southeastern Algeria  

作　　者：Samir Boudibi Bachir Sakaa Zineeddine Benguega Haroun Fadlaoui Tarek Othman Narimen Bouzidi 

机构地区：[1]Laboratory of Ecosystem Diversity and Agricultural Production System Dynamics in Arid Zones(DEDSPAZA),Department of Agricultural Sciences,Mohamed Khider University,Biskra,Algeria [2]Scientific and Technical Research Center on Arid Regions(CRSTRA),Campus of Mohamed Khider University,BP 1682 RP,07000 Biskra,Algeria

出　　处：《Acta Geochimica》2021年第3期390-408,共19页地球化学学报（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 of0.6,and R of 0.968.

关 键 词：Soil salinity COKRIGING Multilayer perceptron Machine learning El-Outaya plain 

分 类 号：S156.4[农业科学—土壤学]