Abstract
Artificial neural networks for estimating the soil water retention curve have been developed considering measured data and require a large quantity of soil samples because only retention curve data obtained for the same set of matric potentials can be used. In order to preclude this drawback, we present two ANN models which tested the performance of ANNs trained with fitted water contents data. These models were compared to a recent new ANN approach for predicting water retention curve, the pseudocontinuous pedotransfer functions (PTFs), which is also an attempt to deal with limited data. Additionally, a sensitivity analysis was carried out to verify the influence of each input parameter on each output. Results showed that fitted ANNs provided similar statistical indexes in predicting water contents to those obtained by the pseudocontinuous method. Sensitivity analysis revealed that bulk density and porosity are the most important parameters for predicting water contents in wet regime, whereas sand and clay contents are more significant in drier conditions. The sensitivity analysis for the pseudocontinuous method demonstrated that the natural logarithm of the matric potential became the most important parameter, and the influences of all other inputs were reduced to be not relevant, except the bulk density.
Highlights
Modelling water flow and solute transport in vadose zone is generally done by means of Richard’s equation and convection-dispersion equation (CDE), respectively, which in turn require some crucial soil information, such as unsaturated soil hydraulic properties or functions and the soil water retention curve
Corresponding data on bulk density (Bd), total porosity (P), particle density (Pd), sand, silt, and clay percentages, and the soil water retention curve measured points were selected and, the first six properties were assigned as input parameters and the soil moisture of the retention curve was assigned as the output parameter for the three developed artificial neural networks (ANNs)
The results obtained for the reference model (ANNo) provided root mean square of error (RMSE) of 0.088 cm3 cm−3, which could be considered satisfactory when compared to other studies
Summary
Modelling water flow and solute transport in vadose zone is generally done by means of Richard’s equation and convection-dispersion equation (CDE), respectively, which in turn require some crucial soil information, such as unsaturated soil hydraulic properties or functions and the soil water retention curve. Knowledge of these properties is needed, for instance, when dealing with irrigation and drainage management, analysis of biological reactions, plant activity, and stream water chemistry [1]. The prediction accuracy of these models hinges upon the quality of the model parameters. Direct measurements of these properties at any grid scale are labour intensive, expensive, and timeconsuming. The more reliable the analysis of the variability of these spatially distributed parameters, the larger the number of samples to be collected [2]. The relations between soil properties and soil-water processes are believed to be highly nonlinear, and in most of the cases they cannot be modelled by simple mathematical formulations or even by complex models, which need a high number of input parameters
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