Combination of artificial neural networks and fractal theory to predict soil water retention curve

2013 ◽  
Vol 92 ◽  
pp. 92-103 ◽  
Author(s):  
Hossein Bayat ◽  
Mohammad Reza Neyshaburi ◽  
Kourosh Mohammadi ◽  
Nader Nariman-Zadeh ◽  
Mahdi Irannejad ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soil Water ◽  
Water Retention ◽  
Fractal Theory ◽  
Water Retention Curve ◽  
Soil Water Retention Curve ◽  
Soil Water Retention ◽  
Retention Curve ◽  
Artificial Neural

scholarly journals Artificial Neural Networks for Predicting the Water Retention Curve of Sicilian Agricultural Soils

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1431 ◽  
Author(s):  
Alessandro D’Emilio ◽  
Rosa Aiello ◽  
Simona Consoli ◽  
Daniela Vanella ◽  
Massimo Iovino
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soil Water ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Water Retention Curve ◽  
Retention Data ◽  
Soil Hydraulic Properties ◽  
Retention Curve ◽  
Artificial Neural

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.

scholarly journals Artificial Neural Networks for Estimating Soil Water Retention Curve Using Fitted and Measured Data

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Tirzah Moreira de Melo ◽  
Olavo Correa Pedrollo
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Artificial Neural Networks ◽  
Water Retention ◽  
Measured Data ◽  
Water Retention Curve ◽  
Soil Water Retention Curve ◽  
Soil Water Retention ◽  
Retention Curve ◽  
Water Contents

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.

INFLUENCE OF SOIL MATRIX ON SOIL-WATER RETENTION CURVE AND HYDRAULIC CHARACTERISTICS

2017 ◽  
Vol 16 (4) ◽  
pp. 869-877
Author(s):  
Vasile Lucian Pavel ◽  
Florian Statescu ◽  
Dorin Cotiu.ca-Zauca ◽  
Gabriela Biali ◽  
Paula Cojocaru
Keyword(s):  
Soil Water ◽  
Water Retention ◽  
Water Retention Curve ◽  
Hydraulic Characteristics ◽  
Soil Water Retention Curve ◽  
Soil Water Retention ◽  
Soil Matrix ◽  
Retention Curve

Modeling Soil Water Retention Curve with a Fractal Method

Pedosphere ◽  
2006 ◽  
Vol 16 (2) ◽  
pp. 137-146 ◽  
Author(s):  
Guan-Hua HUANG ◽  
Ren-Duo ZHANG ◽  
Quan-Zhong HUANG
Keyword(s):  
Soil Water ◽  
Water Retention ◽  
Water Retention Curve ◽  
Soil Water Retention Curve ◽  
Soil Water Retention ◽  
Fractal Method ◽  
Retention Curve
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