Spatial modeling of soil salinity using multiple linear regression, ordinary kriging and artificial neural network methods in the Lower Cheliff plain, Algeria

Abstract

Soil salinity is one of the most damaging environmental issues worldwide, essentially in arid and semi-arid regions, caused by various factors. Spatial estimation and prediction of salinity is important to predict land evaluation in order to develop and determine leaching factor and the precise management for maximum production. The Lower Cheliff is characterized by the augmentation of rate of soil salinity in 80% of area. In this study, the relationship between both elevation and soil salinity was analysed, giving their role in understanding and estimating the spatial distribution of soil salinity in the Lower Cheliff plain. To conduct this work, 406 samples were taken and analysis of electric conductivity was performed as well as the measurement of the elevation using a GPS. The correlations of soil salinity with elevation were analysed as well. In this study, a great focus on the use of the multiple linear regressions, ordinary kriging and artificial neural network methods was given. The results showed that soil salinity had a good correlation with elevation, and according to the values of coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE), the superiority of MLP model was implied with the value of R2 = 0.994, RMSE = 0.63 and MAE = 0.33.