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

被引：6

作者：

de Carvalho, Daniel F. ^{[1
]}

Khoury Junior, Joseph K. ^{[2
]}

Varella, Carlos A. A. ^{[1
]}

Giori, Jacqueline Z.

Machado, Roriz L. ^{[3
]}

机构：

[1] UFRRJ, Inst Tecnol, Dept Engn, BR-465 Seropedica, RJ, Brazil

[2] Univ Fed Vicosa, Dept Engn Prod & Mecan, Vicosa, MG, Brazil

[3] Inst Fed Educ Ciencia & Tecnol Goiano, Ceres, Go, Brazil

来源：

ENGENHARIA AGRICOLA | 2012年 / 32卷 / 01期

关键词：

geographic information system; interpolation methods; soil conservation; VARIABILITY;

D O I：

10.1590/S0100-69162012000100020

中图分类号：

S2 [农业工程];

学科分类号：

0828 ;

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.

引用

页码：197 / 207

页数：11

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