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International Journal of Agronomy
Volume 2010, Article ID 365249, 7 pages
http://dx.doi.org/10.1155/2010/365249
Research Article

Assessing Rainfall Erosivity with Artificial Neural Networks for the Ribeira Valley, Brazil

1Campus Experimental de Registro, UNESP-Universidade Estadual Paulista, 11900-000 Registro, SP, Brazil
2Department of Soil Science, UFLA, Caixa Postal 3037, 37200-000 Lavras, MG, Brazil
3Department of Phytopathology, UFLA, Caixa Postal 3037, 37200-000 Lavras, MG, Brazil

Received 17 March 2010; Revised 22 June 2010; Accepted 14 July 2010

Academic Editor: Bernd Lennartz

Copyright © 2010 Reginald B. Silva 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.

Abstract

Soil loss is one of the main causes of pauperization and alteration of agricultural soil properties. Various empirical models (e.g., USLE) are used to predict soil losses from climate variables which in general have to be derived from spatial interpolation of point measurements. Alternatively, Artificial Neural Networks may be used as a powerful option to obtain site-specific climate data from independent factors. This study aimed to develop an artificial neural network to estimate rainfall erosivity in the Ribeira Valley and Coastal region of the State of São Paulo. In the development of the Artificial Neural Networks the input variables were latitude, longitude, and annual rainfall and a mathematical equation of the activation function for use in the study area as the output variable. It was found among other things that the Artificial Neural Networks can be used in the interpolation of rainfall erosivity values for the Ribeira Valley and Coastal region of the State of São Paulo to a satisfactory degree of precision in the estimation of erosion. The equation performance has been demonstrated by comparison with the mathematical equation of the activation function adjusted to the specific conditions of the study area.