Abstract
Modeling soil-water regime and solute transport in the vadose zone is strategic for estimating agricultural productivity and optimizing irrigation water management. Direct measurements of soil hydraulic properties, i.e., the water retention curve and the hydraulic conductivity function, are often expensive and time-consuming, and represent a major obstacle to the application of simulation models. As a result, there is a great interest in developing pedotransfer functions (PTFs) that predict the soil hydraulic properties from more easily measured and/or routinely surveyed soil data, such as particle size distribution, bulk density (ρb), and soil organic carbon content (OC). In this study, application of PTFs was carried out for 359 Sicilian soils by implementing five different artificial neural networks (ANNs) to estimate the parameter of the van Genuchten (vG) model for water retention curves. The raw data used to train the ANNs were soil texture, ρb, OC, and porosity. The ANNs were evaluated in their ability to predict both the vG parameters, on the basis of the normalized root-mean-square errors (NRMSE) and normalized mean absolute errors (NMAE), and the water retention data. The Akaike’s information criterion (AIC) test was also used to assess the most efficient network. Results confirmed the high predictive performance of ANNs with four input parameters (clay, sand, and silt fractions, and OC) in simulating soil water retention data, with a prediction accuracy characterized by MAE = 0.026 and RMSE = 0.069. The AIC efficiency criterion indicated that the most efficient ANN model was trained with a relatively low number of input nodes.
Highlights
Soil hydraulic properties are important for simulating water availability and transmission in soils.An important hydraulic property of the soil is the water retention capacity, which affects productivity and soil management
The analysis of the indicators showed that all the artificial neural networks (ANNs) were efficient in estimating parameter α with normalized root-mean-square errors (NRMSE) and normalized mean absolute errors (NMAE) values in the range of 0.068–0.071 and 0.039–0.043, respectively
The lowest performance was obtained for θ r with NRMSE and NMAE values in the range 0.24–0.27 and 0.20–0.23, respectively
Summary
An important hydraulic property of the soil is the water retention capacity, which affects productivity and soil management. The availability of accessible and representative soil hydraulic properties is generally a major obstacle to understanding the dynamics of water and solutes in the unsaturated soil [1] and the application of simulation models to prevent and control deterioration of soil due to intensive agricultural activities. Water 2018, 10, 1431 the unsaturated hydraulic conductivity function [2], which in turn allow modeling of water flow in the vadose zone. Due to their high spatial variability, determination of these properties requires a larger number of soil samples, implying expensive and time-consuming field and laboratory analyses [3]
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