![](/images/graphics-bg.png)
Assessing Rainfall Erosivity with Artificial Neural Networks for the Ribeira Valley, Brazil
Joint Authors
Iori, Piero
Bendini, Hugo N.
Silva, Reginald B.
Armesto, Cecilia
Source
International Journal of Agronomy
Issue
Vol. 2010, Issue 2010 (31 Dec. 2010), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2010-08-24
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Silva, Reginald B.& Iori, Piero& Armesto, Cecilia& Bendini, Hugo N.. 2010. Assessing Rainfall Erosivity with Artificial Neural Networks for the Ribeira Valley, Brazil. International Journal of Agronomy،Vol. 2010, no. 2010, pp.1-7.
https://search.emarefa.net/detail/BIM-466235
Modern Language Association (MLA)
Silva, Reginald B.…[et al.]. Assessing Rainfall Erosivity with Artificial Neural Networks for the Ribeira Valley, Brazil. International Journal of Agronomy No. 2010 (2010), pp.1-7.
https://search.emarefa.net/detail/BIM-466235
American Medical Association (AMA)
Silva, Reginald B.& Iori, Piero& Armesto, Cecilia& Bendini, Hugo N.. Assessing Rainfall Erosivity with Artificial Neural Networks for the Ribeira Valley, Brazil. International Journal of Agronomy. 2010. Vol. 2010, no. 2010, pp.1-7.
https://search.emarefa.net/detail/BIM-466235
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-466235