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Modelling soil water retention using support vector machines with genetic algorithm optimisation

AuthorsLamorski, Krzysztof; Sławiński, Cezary; Moreno Lucas, Félix CSIC ORCID; Barna, Gyöngyi; Skierucha, Wojciech; Arrúe Ugarte, José Luis CSIC ORCID
Issue DateMar-2014
CitationLamorkski K, Sławiński C, Moreno F, Barna G, Skierucha W, Arrúe JL. Modelling soil water retention using support vector machines with genetic algorithm optimisation. The Scientific World Journal Vol. 2014: Article ID 740521 (2014)
AbstractThis work presents point pedotransfer function (PTF) models of the soil water retention curve. The developed models allowed for estimation of the soil water content for the specified soil water potentials: –0.98, –3.10, –9.81, –31.02, –491.66, and –1554.78 kPa, based on the following soil characteristics: soil granulometric composition, total porosity, and bulk density. Support Vector Machines (SVM) methodology was used for model development. A new methodology for elaboration of retention function models is proposed. Alternative to previous attempts known from literature, the -SVM method was used for model development and the results were compared with the formerly used the -SVM method. For the purpose of models’ parameters search, genetic algorithms were used as an optimisation framework. A new form of the aim function used for models parameters search is proposed which allowed for development of models with better prediction capabilities. This new aim function avoids overestimation of models which is typically encountered when root mean squared error is used as an aim function. Elaborated models showed good agreement with measured soil water retention data. Achieved coefficients of determination values were in the range 0.67–0.92. Studies demonstrated usability of -SVM methodology together with genetic algorithm optimisation for retention modelling which gave better performing models than other tested approaches.
Description10 Pags.- 3 Tabls.- 4 Figs.- Copyright © 2014 Krzysztof Lamorski et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher version (URL)http://dx.doi.org/10.1155/2014/740521
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