Soil Water Content Modeling using Artificial Neural Network in Arid Soils of Algeria

Background: In order to model ecological and hydrological processes and schedule irrigation as efficiently as possible, soil water contents at field capacity (FC) and permanent wilting point (PWP) across a landscape are crucial. However, it takes a lot of time and money to measure them directly. An alternative is offered by pedotransfer functions (PTFs), which forecast them based on readily available or simpler soil variables. Methods: The purpose of this study was to compare the performance of FC and PWP models, developed with artificial neural networks (ANN) to multiple linear regression (MLR). A total of 120 soil samples from various Biskra province locations were used. The developed models are evaluated using the statistical criteria of mean error (ME), coefficient of determination (R2) and root mean square error (RSME). Result: The results show that the ANN performs better than the traditional MLR. The obtened results were very positive and indicated that the ANN approach held promise for forecasting soil water contents in our area.