RAINFALL EROSIVITY FOR THE STATE OF RIO DE JANEIRO ESTIMATED BY ARTIFICIAL NEURAL NETWORK

de Carvalho Daniel F.; Khoury Junior Joseph K.; Varella Carlos A. A.Giori Jacqueline Z.Machado Roriz L.

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RAINFALL EROSIVITY FOR THE STATE OF RIO DE JANEIRO ESTIMATED BY ARTIFICIAL NEURAL NETWORK

机译：RAINFALL EROSIVITY FOR THE STATE OF RIO DE JANEIRO ESTIMATED BY ARTIFICIAL NEURAL NETWORK

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abstract_textpThe 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./p/abstract_text

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《engenharia agricola》 |2012年第1期|197-207|共11页
作者
de Carvalho Daniel F.; Khoury Junior Joseph K.; Varella Carlos A. A.Giori Jacqueline Z.Machado Roriz L.;
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作者单位

UFRRJ, Inst Tecnol, Dept Engn, BR-465 Seropedica, RJ, Brazil;

Univ Fed Vicosa, Dept Engn Prod & Mecan, Vicosa, MG, Brazil;

Inst Fed Educ Ciencia & Tecnol Goiano, Ceres, Go, Brazil;

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正文语种英语
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geographic information system; interpolation methods; soil conservation; VARIABILITY;

RAINFALL EROSIVITY FOR THE STATE OF RIO DE JANEIRO ESTIMATED BY ARTIFICIAL NEURAL NETWORK

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