Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks
| Autor: | ROBERTO A. CECILIO, MICHEL C. MOREIRA, JOSE EDUARDO M. PEZZOPANE, FERNANDO F. PRUSKI, DANILO C. FUKUNAGA |
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| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: | |
| Zdroj: | Anais da Academia Brasileira de Ciências, Vol 85, Iss 4, Pp 1523-1535 (2013) |
| Druh dokumentu: | article |
| ISSN: | 1678-2690 0001-3765 |
| DOI: | 10.1590/0001-3765201398012 |
| Popis: | The rainfall parameter that expresses the capacity to promote soil erosion is called rainfall erosivity (R), and is commonly represented by the indexes EI30 and KE>25. The calculations of these indexes requires pluviographical records, that are difficult to obtain in Brazil. This paper describes the use of synthetic rainfall series to compute EI30 and KE>25 in Espírito Santo State (Brazil). Artificial neural networks (ANNs) were also developed to spatially interpolate R values in Espírito Santo. EI30 and KE>25 indexes values were close to those calculated on a homogeneous area according to the similarity of rainfall distribution; indicating the applicability of the use of synthetic rainfall series to estimate the R factor. ANNs had a better performance than Inverse Distance Weighted and Kriging to spatially interpolate rainfall erosivity values in the State of Espírito Santo. |
| Databáze: | Directory of Open Access Journals |
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