dc.creatorMoreira, Michel Castro
dc.creatorOliveira, Thiago Emanuel Cunha de
dc.creatorCecílio, Roberto Avelino
dc.creatorPinto, Francisco de Assis de Carvalho
dc.creatorPruski, Fernando Falco
dc.date2017-12-04T09:46:44Z
dc.date2017-12-04T09:46:44Z
dc.date2016-09-19
dc.date.accessioned2023-09-27T21:04:58Z
dc.date.available2023-09-27T21:04:58Z
dc.identifier1806-9657
dc.identifierhttp://dx.doi.org/10.1590/18069657rbcs20150132
dc.identifierhttp://www.locus.ufv.br/handle/123456789/14290
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8954201
dc.descriptionWater erosion is the process of disaggregation and transport of sediments, and rainfall erosivity is a numerical value that expresses the erosive capacity of rain. The scarcity of information on rainfall erosivity makes it difficult or impossible to use to estimate losses occasioned by the erosive process. The objective of this study was to develop Artificial Neural Networks (ANNs) for spatial interpolation of the monthly and annual values of rainfall erosivity at any location in the state of Rio Grande do Sul, and a software that enables the use of these networks in a simple and fast manner. This experiment used 103 rainfall stations in Rio Grande do Sul and their surrounding area to generate synthetic rainfall series on the software ClimaBR 2.0. Rainfall erosivity was determined by summing the values of the EI30 and KE >25 indexes, considering two methodologies for obtaining the kinetic energy of rainfall. With these values of rainfall erosivity and latitude, longitude, and altitude of the stations, the ANNs were trained and tested for spatializations of rainfall erosivity. To facilitate the use of the ANNs, a computer program was generated, called netErosividade RS, which makes feasible the use of ANNs to estimate the values of rainfall erosivity for any location in the state of Rio Grande do Sul.
dc.formatpdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherRevista Brasileira de Ciência do Solo
dc.relationv. 40, e0150132, Sep. 2016
dc.rightsOpen Access
dc.subjectErosive potential of rainfall
dc.subjectSoil conservation
dc.subjectUniversal soil loss equation
dc.titleSpatial interpolation of rainfall erosivity using artificial nbeural networks for southern Brazil conditions
dc.typeArtigo


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