ABSTRACT Water 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.

A erosividade da chuva é um índice numérico que expressa a capacidade das chuvas em provocar erosão hídrica no solo. O presente trabalho teve por objetivo desenvolver um programa computacional para estimar os valores da erosividade da chuva no Estado de Minas Gerais com base em redes neurais artificiais (RNAs). O valor anual da erosividade da chuva é obtido pelo somatório dos valores mensais dos índices de erosividade EI30 ou KE > 25. Foram utilizados para cálculo de cada um desses índices dois métodos de obtenção da energia cinética de precipitação pluvial. Dessa maneira, obtiveram-se quatro valores de erosividade para cada mês, totalizando o desenvolvimento de 48 redes. As RNAs desenvolvidas foram implementadas no ambiente de programação Borland Delphi 7.0. O programa computacional desenvolvido foi denominado NetErosividade MG. O programa fornece, de forma fácil e rápida, os valores mensais e anual da erosividade da chuva para qualquer localidade do Estado de Minas Gerais.

Rainfall erosivity represents the potential of rainfall causing soil erosion. This study aimed to develop a software to estimate rainfall erosivity in the state of Minas Gerais based on Artificial Neural Networks (ANNs). The annual value of the rainfall erosivity is given by the sum of the monthly values of the erosivity indexes EI30 or KE > 25. Two methodologies were used to estimate the kinetic energy for each index. Thus, four erosivity values were evaluated for each month, resulting in the development of 48 ANNs. These ANNs were implemented using the software Borland Delphi 7.0. The new software was called NetErosividade MG. The program calculates the monthly and annual values of rainfall erosivity for any location in the state of Minas Gerais in an easy and fast way.