Neural Networks Prediction of Soil Hydraulic Functions for Alluvial Soils Using Multistep Outflow Data

Abstract

Indirect methods for prediction of soil hydraulic properties play an important role in understanding site‐specific unsaturated water flow and transport processes, usually via numerical simulation models. Specifically, pedotransfer functions (PTFs) to predict soil‐water retention have been widely developed. However, few datasets that include unsaturated hydraulic conductivity data are available for prediction purposes. Moreover, those available employ a variety of measurement techniques. We show that prediction of soil‐water retention and unsaturated hydraulic conductivity curves from basic soil properties can be improved if hydraulic data are determined by a single measurement method that is consistently applied to all soil samples. Here, we present a unique dataset that consists of 310 soil‐water retention and unsaturated hydraulic conductivity functions, all of which were obtained from the multistep outflow method. With this dataset, neural networks coupled with bootstrap aggregation were used to predict the soil‐water retention and hydraulic conductivity characteristics from basic soil properties, that is, sand, silt, and clay content, bulk density (ρb), saturated water content, and saturated hydraulic conductivity. The prediction errors of water content were about 3 to 4% by volume. Unsaturated hydraulic conductivity predictions improved significantly when a so‐called performance‐based algorithm was utilized to minimize residuals of soil hydraulic data rather than hydraulic parameters. The root mean squared of residuals for predicted values of water content and unsaturated hydraulic conductivity were reduced by about 50% when compared with predicted hydraulic functions using a published neural networks program Rosetta Results from a sensitivity analysis suggest that the hydraulic parameters are mostly sensitive to sand content and saturated water content.

Selection of a suitable model for the prediction of soil water content in north of Iran

“…The point PTFs derived from multiple linear regression (MLR) method (training data). Selection of a suitable model for the prediction of soil water content in north of Iran result is in line with the work done by Minasny et al (2004) and Amini et al (2005). But, with regard to evaluation criteria, our study is more accurate than other mentioned researches, because, selection of inputs and model designing (such as type of learning algorithm, the number of layers and neurons, etc,) is carried out more accurately.…”

“…Recently, an alternative, indirect estimation of soil hydraulic properties from widely available or more easily measured basic soil properties using pedotransfer functions (PTFs) has attracted considerable attention of researchers in a variety of fields such as soil scientists, hydrologists, and agricultural and environmental engineers (Minasny et al, 2004;Huang et al, 2010).PTFs are based on physical approaches or on empirical regression equations that link soil physical and/or chemical 2 the ANN method showed good prediction function, and it would play a greater role in the prediction of soil hydraulic properties with the improvement of scientific technology. Najafi & Givi (2006) used the ANN and PTF methods for prediction of soil bulk density.…”

“…The Rosetta model based on ANNs works in a hierarchical approach which employs five different PTFs to predict water retention curves (Schaap et al, 2001).Rosetta software is used widely by many scientists for the estimation of soil water retention (SWR), saturated hydraulic conductivity and unsaturated hydraulic conductivity parameters (Schaap et al, 2001;Givi et al, 2004;Minasny et al, 2004;Stumpp et al, 2009).…”

“…Function PTFs predict the parameters of a closed-form analytical equation or empirical parameters of water retention curve models, such as the model of Brooks & Corey (1964) or the van Genuchten equation (Van Genuchten, 1980). At present, there are two common methods to develop PTFs for point and/or function estimations, which are the Multiple Linear Regression (MLR) method (Merdun et al, 2006) and the Artificial Neural Network (ANN) method (Minasny et al, 2004;Sarmadian & Mehrjardi, 2008). In recent years, the ANN method was used to improve the prediction accuracy of PTFs (Kumar et al, 2010;Rezaei-Arshad et al, 2013;Moghimi et al, 2014;Mukhlisin & Abd Rahman, 2014;Rezaeianzadeh et al, 2014).…”

“…However, this method also has some significant disadvantages which must be taken into consideration (Minasny et al, 2004). First of all, the interpretation of ANN is often difficult and subjective, because the fitting with the transfer function is a black-box approach.…”