Research Article Modelling Soil Water Retention Using Support Vector Machines with Genetic Algorithm Optimisation Krzysztof Lamorski, 1 Cezary SBawiNski, 1 Felix Moreno, 2 Gyöngyi Barna, 3 Wojciech Skierucha, 1 and José L. Arrue 4 1 Department of Metrology and Modelling of Agrophysical Processes, Institute of Agrophysics, Polish Academy of Sciences, Do´ swiadczalna 4, 20-290 Lublin, Poland 2 Institute for Natural Resources and Agrobiology (IRNAS-CSIC), P.O. Box 1052, 41080 Sevilla, Spain 3 Department of Crop Production and Soil Science, Georgikon Faculty, University of Pannonia, De´ ak Ferenc Street 16, Keszthely, 8360, Hungary 4 Aula Dei Experimental Station (EEAD-CSIC), P.O. Box 13034, 50080 Zaragoza, Spain Correspondence should be addressed to Krzysztof Lamorski; k.lamorski@ipan.lublin.pl Received 19 November 2013; Accepted 29 January 2014; Published 17 March 2014 Academic Editors: N. Moritsuka and G. Pietramellara Copyright © 2014 Krzysztof Lamorski et al. his 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. his work presents point pedotransfer function (PTF) models of the soil water retention curve. he developed models allowed for estimation of the soil water content for the speciied 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. his 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 coeicients 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. 1. Introduction Soil hydrologic parameters have great impact on soil water transport processes. he soil water retention curve and soil water hydraulic conductivity are required for an appropriate description of soil water phenomena, such as drainage, iniltration, or soil pollutant movement. he retention curve describes the relationship between soil water content and soil water potential and is especially important for hydrological modelling and agronomical practice as it determines soil water availability for plants. Measurements give strict evaluation of hydraulic prop- erties of soils. Unfortunately, measurement of the soil water retention curve is time consuming and requires specialised equipment. he classical pressure plate extractor technique [1] may be used to determine the soil water retention curve, or an alternative technique based on the dynamic simultaneous time-domain relectometry soil water content and pressure head measurements [2]. Fortunately in many applications the hydraulic param- eters can be estimated rather than measured. Pedotransfer functions (PTF) are commonly used in such circumstances [3] and allow for estimation of the retention curve or hydraulic conductivity based on easily measured soil char- acteristics. he most widely used soil characteristics for PTF Hindawi Publishing Corporation e Scientific World Journal Volume 2014, Article ID 740521, 10 pages http://dx.doi.org/10.1155/2014/740521