Testing an Artificial Neural Network for Predicting Soil Hydraulic Conductivity

Abstract

Multilinear regression has been used extensively to predict soil hydraulic properties, both the θ(h) and K(h) relationships, from easily obtainable soil variables. As an alternative, this study investigated the performance of an artificial radial basis neural network in predicting some K(h) values from other variables. This kind of neural network may be seen as a multivariate interpolation technique, which can theoretically fit any nonlinear continuous function. Neural networks are characterized by parameters that must be optimized to solve a given problem. We used a fitting procedure requiring only two parameters to ensure a unique solution. These two parameters were determined by data splitting. Hypothetical data bases with uncertainties were simulated to analyze the performance of the neural network in predicting a nonlinear relation derived from a classical model for K(h). A soil data base covering a broad spectrum of soil horizons was used to test the neural network in solving multivariate problems. Numerical simulations showed that the neural network was sensitive to large uncertainties in the data base. It was more efficient than a multilinear regression when the uncertainties were small. Experimental results showed that the neural network was more efficient than the multilinear regression for predicting K(h = −1 m) or K(h = −2.5 m) from two qualitative and five quantitative soil variables. It was also more efficient than two independent multilinear regressions, one for the sandy samples and the other for the loamy and clayey samples. Provided that a large data base with accurate K values is available, artificial neural networks can be useful to predict θ(h) and K(h) over a broad spectrum of soils.

Comparison of Three Regression Models for Determining Water Retention Curves

“…Besides the standard regression methods, artificial neural networks (ANNs) have become the tool of choice in the last decade in developing PTFs, e.g., Schaap et al, 1998;Pachepsky et al, 1996;Tamari et al, 1996 etc.. The above authors confirm that they received better results from ANN-based pedotransfer functions than from standard regression-based PTFs.…”

“…Recent developments in machine learning methods have forced the application of alternative datadriven methods in hydrology applications, e.g., radial basis function networks (Tamari et al, 1996;Kumar et al, 2010) or support vector machines (Lamorski et al, 2008;Twarakawi et al, 2009). The foundations of support vector machines (SVMs) were developed by Vapnik (1995) and are gaining in popularity due to their attractive features and promising empirical performance.…”

Evaluation of adaptive neural-based fuzzy inference system approach for estimating saturated soil water content

“…Multiple-linear regression method (Mayr and Jarvis 1999; Tomasella et al 2000), group Method of Data Handling (Pachepsky and Rawls 1999) and neural network analysis (ANN) (Schaap et al 1998;Minasny and McBratney 2002;Minasny et al 2004) have been used to develop h s PTFs. Tamari et al (1996) have presented a review of ANN applicability to estimate soil hydraulic properties. Two problems that should be concerned in developing ANN-based models are included: (1) the learning algorithm may not get optimum weights to minimize prediction errors, (2) a number of weights that are difficult to easy interpretation (Schaap et al 2001).…”

Determining Soil – Water Content by Data Driven Modeling When Relatively Small Data Sets Are Available

“…Besides the standard regression methods, artificial neural networks (ANNs) have become the tool of choice in developing PTFs, e.g., [1], [5], [7], [10], etc.). Authors of above works confirm that they received better results from ANN-based pedotransfer functions than from standard linear regression-based PTFs.…”