English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/94383
Compartir / Impacto:
Estadísticas
Add this article to your Mendeley library MendeleyBASE
Citado 4 veces en Web of Knowledge®  |  Pub MebCentral Ver citas en PubMed Central  |  Ver citas en Google académico
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar otros formatos: Exportar EndNote (RIS)Exportar EndNote (RIS)Exportar EndNote (RIS)
Título : Modelling soil water retention using support vector machines with genetic algorithm optimisation
Autor : Lamorski, Krzysztof; Sławiński, Cezary; Moreno Lucas, Félix ; Barna, Gyöngyi; Skierucha, Wojciech; Arrúe Ugarte, José Luis
Fecha de publicación : mar-2014
Citación : Lamorkski 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)
Resumen: This 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.
Descripción : 10 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.
Versión del editor: http://dx.doi.org/10.1155/2014/740521
URI : http://hdl.handle.net/10261/94383
DOI: 10.1155/2014/740521
E-ISSN: 1537-744X
Aparece en las colecciones: (EEAD) Artículos
(IRNAS) Artículos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
ArrueJL_SciWorldJ_2014.pdf1,93 MBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo
 



NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.