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Richarde Marques da Silva

Researcher at Federal University of Paraíba

Publications -  129
Citations -  1889

Richarde Marques da Silva is an academic researcher from Federal University of Paraíba. The author has contributed to research in topics: Land cover & Environmental science. The author has an hindex of 19, co-authored 106 publications receiving 1121 citations.

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Rainfall and river flow trends using Mann–Kendall and Sen’s slope estimator statistical tests in the Cobres River basin

TL;DR: In this paper, the authors used the nonparametric Mann-Kendall and Sen's methods to determine whether there was a positive or negative trend in rainfall data with their statistical significance.
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Spatiotemporal impact of land use/land cover changes on urban heat islands: A case study of Paço do Lumiar, Brazil

TL;DR: In this article, the intensity and accelerated growth of urban heat islands (UHI) were investigated, and changes in land use/land cover (LULC) were detected over 16 years based on multi-temporal Landsat TM satellite data.
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Future scenarios based on a CA-Markov land use and land cover simulation model for a tropical humid basin in the Cerrado/Atlantic forest ecotone of Brazil

TL;DR: In this article, the authors analyzed the future changes in land use and land cover of the advancement of agriculture in the native vegetation areas of the Cerrado/Atlantic forest ecotone in the Prata River basin in 2033, 2050, 2080 and 2100.
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Modeling land cover change based on an artificial neural network for a semiarid river basin in northeastern Brazil

TL;DR: In this paper, the authors used a multilayer perceptron (MLP) neural network to analyze changes in land cover and to estimate a future scenario for 2035 using an artificial neural network for the Taperoa River basin.
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Drought assessment using a TRMM-derived standardized precipitation index for the upper São Francisco River basin, Brazil

TL;DR: An alternative classification is proposed to characterize the drought, which spatially categorizes the drought type (short-, medium-, and long-term) according to the analyzed drought event type (extreme, severe, moderate, and mild).