SciELO - Scientific Electronic Library Online

 
vol.44 issue2Iron oxides and specific surface area of the subtropical Oxisol under native pasture and forestDietary protein levels in Piaractus brachypomus submitted to extremely acidic or alkaline pH author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

Share


Ciência Rural

Print version ISSN 0103-8478

Abstract

SOARES, Fátima Cibele et al. Artificial neural networks to estimate soil water retention. Cienc. Rural [online]. 2014, vol.44, n.2, pp.293-300. ISSN 0103-8478.  http://dx.doi.org/10.1590/S0103-84782014000200016.

The study aims to propose a methodology for estimating the water retention curve for soils of the State of Rio Grande do Sul, by using artificial neural networks. For the development of the research it was assembled a database with information available in the literature, texture and structure of soils of Rio Grande do Sul. The modeling was developed using the software Matlab, where the networks were trained with different architectures, varying the numbers of neurons in the input layer and the hidden layer. The efficiency of the network was analyzed graphically by the ratio 1:1 between the estimated versus the observed data by means of statistical indicators. It was observed from the results that the architecture with best predictive performance was the 4-24-7, with index classification of "great" performance. Thus it can be inferred that the use of neural networks to estimate the water retention curve of the soil is a tool with high predictive ability which will bring great contribution to the agricultural sector.

Keywords : pedofunctions; artificial intelligence; soil moisture; matric potential.

        · abstract in Portuguese     · text in Portuguese     · Portuguese ( pdf )

 

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License