Rainfall erosivity for the State of Rio de Janeiro estimated by artificial neural network

Engenharia Agrícola

Daniel F. De Carvalho²  Joseph K. Khoury Júnior¹  Carlos A. A. Varella¹  Jacqueline Z. Giori¹  Roriz L. Machado¹ 

[1] ,UFRRJ Instituto de Tecnologia Departamento de Engenharia

关键词: geographic information system;    interpolation methods;    soil conservation;    sistema de informação geográfica;    métodos de interpolação;    conservação de solo;   

DOI  :  10.1590/S0100-69162012000100020

来源: SciELO

【 摘 要 】

The Artificial Neural Networks (ANNs) are mathematical models method capable of estimating non-linear response plans. The advantage of these models is to present different responses of the statistical models. Thus, the objective of this study was to develop and to test ANNs for estimating rainfall erosivity index (EI30) as a function of the geographical location for the state of Rio de Janeiro, Brazil and generating a thematic visualization map. The characteristics of latitude, longitude e altitude using ANNs were acceptable to estimating EI30 and allowing visualization of the space variability of EI30. Thus, ANN is a potential option for the estimate of climatic variables in substitution to the traditional methods of interpolation.

【 授权许可】

CC BY   
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