Assessing Rainfall Erosivity with Artificial Neural Networks for the Ribeira Valley, Brazil
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. Alter...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2010-01-01
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| Series: | International Journal of Agronomy |
| Online Access: | http://dx.doi.org/10.1155/2010/365249 |
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| author | Reginald B. Silva Piero Iori Cecilia Armesto Hugo N. Bendini |
| author_facet | Reginald B. Silva Piero Iori Cecilia Armesto Hugo N. Bendini |
| author_sort | Reginald B. Silva |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-25b8da75cea344889f442e16997fdd63 |
| institution | Kabale University |
| issn | 1687-8159 1687-8167 |
| language | English |
| publishDate | 2010-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Agronomy |
| spelling | doaj-art-25b8da75cea344889f442e16997fdd632025-08-20T03:54:52ZengWileyInternational Journal of Agronomy1687-81591687-81672010-01-01201010.1155/2010/365249365249Assessing Rainfall Erosivity with Artificial Neural Networks for the Ribeira Valley, BrazilReginald B. Silva0Piero Iori1Cecilia Armesto2Hugo N. Bendini3Campus Experimental de Registro, UNESP-Universidade Estadual Paulista, 11900-000 Registro, SP, BrazilDepartment of Soil Science, UFLA, Caixa Postal 3037, 37200-000 Lavras, MG, BrazilDepartment of Phytopathology, UFLA, Caixa Postal 3037, 37200-000 Lavras, MG, BrazilCampus Experimental de Registro, UNESP-Universidade Estadual Paulista, 11900-000 Registro, SP, BrazilSoil 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.http://dx.doi.org/10.1155/2010/365249 |
| spellingShingle | Reginald B. Silva Piero Iori Cecilia Armesto Hugo N. Bendini Assessing Rainfall Erosivity with Artificial Neural Networks for the Ribeira Valley, Brazil International Journal of Agronomy |
| title | Assessing Rainfall Erosivity with Artificial Neural Networks for the Ribeira Valley, Brazil |
| title_full | Assessing Rainfall Erosivity with Artificial Neural Networks for the Ribeira Valley, Brazil |
| title_fullStr | Assessing Rainfall Erosivity with Artificial Neural Networks for the Ribeira Valley, Brazil |
| title_full_unstemmed | Assessing Rainfall Erosivity with Artificial Neural Networks for the Ribeira Valley, Brazil |
| title_short | Assessing Rainfall Erosivity with Artificial Neural Networks for the Ribeira Valley, Brazil |
| title_sort | assessing rainfall erosivity with artificial neural networks for the ribeira valley brazil |
| url | http://dx.doi.org/10.1155/2010/365249 |
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