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All publications of “Eliana de Souza”

3 results

Spatial and Temporal Potential Groundwater Recharge: the Case of the Doce River Basin, Brazil

Eliana de Souza ORCID logo , Lucas Machado Pontes, Elpídio Inácio Fernandes Filho, Carlos Ernesto Goncalves Reynaud Schaefer, Eliana Elizabet dos Santos


ABSTRACT: Little is known about the groundwater recharge potential of weathered tropical catchments, where increasing water uptake is widespread to meet various water demands. This study aimed to estimate the volume of groundwater recharge of the Doce River Basin, Minas Gerais, Brazil. The BALSEQ model was applied to calculate the water balance over a period of two years (2007-2009). Evapotranspiration, runoff, and potential groundwater recharge (PGR) were calculated, using daily data on rainfall, potential evapotranspiration, and plant-available water. A soil […]

Digital Soil Mapping Using Machine Learning Algorithms in a Tropical Mountainous Area

Martin Meier, Eliana de Souza, Marcio Rocha Francelino, Elpídio Inácio Fernandes Filho, Carlos Ernesto Gonçalves Reynaud Schaefer


ABSTRACT: Increasingly, applications of machine learning techniques for digital soil mapping (DSM) are being used for different soil mapping purposes. Considering the variety of models available, it is important to know their performance in relation to soil data and environmental variables involved in soil mapping. This paper investigated the performance of eight machine learning algorithms for soil mapping in a tropical mountainous area of an official rural settlement in the Zona da Mata region in Brazil. Morphometric maps generated from […]

Multinomial Logistic Regression and Random Forest Classifiers in Digital Mapping of Soil Classes in Western Haiti

Wesly Jeune, Márcio Rocha Francelino, Eliana de Souza, Elpídio Inácio Fernandes Filho, Genelício Crusoé Rocha


ABSTRACT Digital soil mapping (DSM) has been increasingly used to provide quick and accurate spatial information to support decision-makers in agricultural and environmental planning programs. In this study, we used a DSM approach to map soils in western Haiti and compare the performance of the Multinomial Logistic Regression (MLR) with Random Forest (RF) to classify the soils. The study area of 4,300 km2 is mostly composed of diverse limestone rocks, alluvial deposits, and, to a lesser extent, basalt. A soil […]