SOFTWARE TO ESTIMATE RAINFALL EROSIVITY IN ESPÍRITO SANTO STATE
This study reports development of a software capable of applying Artificial Neural Networks (ANNs) to estimate mean monthly and annual rain erosivity (R) in Espírito Santo State. Considering that the monthly value of the R is obtained by summing the monthly erosivity indexes EI30 or KE > 25, and...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2012 |
| País: | Brasil |
| Institución: | Universidade Federal de Viçosa (UFV) |
| Repositorio: | Engenharia na Agricultura |
| Idioma: | portugués |
| OAI Identifier: | oai:ojs.periodicos.ufv.br:article/284 |
| Acceso en línea: | https://periodicos.ufv.br/reveng/article/view/284 |
| Access Level: | acceso abierto |
| Palabra clave: | equação universal de perda de solo Interpolação espacial Manejo de bacias hidrográficas Redes neurais artificiais |
| Sumario: | This study reports development of a software capable of applying Artificial Neural Networks (ANNs) to estimate mean monthly and annual rain erosivity (R) in Espírito Santo State. Considering that the monthly value of the R is obtained by summing the monthly erosivity indexes EI30 or KE > 25, and that there are two methods to obtain the rain kinetic energy, 4 ANNs were used for each month totaling 48 ANNs. It was necessary to know the respective architectures, neuron activation functions and the free parameters w’s and b’s to generate the mathematical functions representing ANNs. ANNs were implemented using the software Borland Delphi 7.0. This software (netErosividade ES) permits fast and easy prediction of monthly and annual R factor for any locality of Espírito Santo state. |
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