Rainfall erosivity for the State of Rio de Janeiro estimated by artificial neural network

Carvalho, Daniel Fonseca de; Khoury, Joseph; Varella, Carlos Alberto Alves; Giori, Jacqueline Zardo; Machado, Roriz Luciano

doi:10.1590/s0100-69162012000100020

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…This way, the greatest erosivity values are generally caused by intense rainfall occurring mainly in the rainy season. Further, Carvalho et al (2012) found similar results of rainfall erosivity for the same study area.…”

Section: Resultssupporting

confidence: 69%

Predicting soil erosion using Rusle and NDVI time series from TM Landsat 5

Carvalho

Durigon

Antunes

et al. 2014

Pesq. agropec. bras.

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-The objective of this work was to evaluate the seasonal variation of soil cover and rainfall erosivity, and their influences on the revised universal soil loss equation (Rusle), in order to estimate watershed soil losses in a temporal scale. Twenty-two TM Landsat 5 images from 1986 to 2009 were used to estimate soil use and management factor (C factor). A corresponding rainfall erosivity factor (R factor) was considered for each image, and the other factors were obtained using the standard Rusle method. Estimated soil losses were grouped into classes and ranged from 0. , respectively. Mean annual soil loss in the watershed was 109.5 Mg ha -1 , but the central area, with a loss of nearly 300.0 Mg ha -1 , was characterized as a site of high water-erosion risk. The use of C factor obtained from remote sensing data, associated to corresponding R factor, was fundamental to evaluate the soil erosion estimated by the Rusle in different seasons, unlike of other studies which keep these factors constant throughout time.Index terms: C factor, rainfall erosivity, remote sensing, soil loss, vegetation index. Predição da erosão do solo com uso da Rusle e séries temporais de NDVI do Landsat 5 TMResumo -O objetivo deste trabalho foi avaliar a variação estacional da cobertura do solo e da erosividade da chuva e suas influências na equação universal de perda de solo revisada (Rusle), para estimar as perdas de solo em uma microbacia, em escala temporal. , mas a área central da bacia, com perdas de aproximadamente 300,0 Mg ha -1 , foi considerada como de alto risco à erosão pela água. O uso do fator C obtido a partir da técnica de sensoriamento remoto, associado ao fator R correspondente, é fundamental para avaliar as perdas de solo estimadas pela Rusle em diferentes épocas do ano, diferentemente de outros estudos em que estes fatores são mantidos constantes ao longo do tempo.Termos para indexação: fator C, erosividade, sensoriamento remoto, perda de solo, índice de vegetação.

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Section: Resultssupporting

confidence: 69%

Predicting soil erosion using Rusle and NDVI time series from TM Landsat 5

Carvalho

Durigon

Antunes

et al. 2014

Pesq. agropec. bras.

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“…La aplicación de redes neurales artificiales (RNAs) ha sido propuesta por varios autores para modelar la precipitación, la evapotranspiración y la humedad (Alves Sobrinho et al 2011;Carvalho et al 2012;Yasar et al 2012). También, Santos et al (2016) utilizaron redes neurales artificiales del tipo perceptron con múltiples camadas para monitorear cerdos y obtuvieron resultados precisos y con adecuada exactitud.…”

Section: Introductionunclassified

Redes neurales artificiales para estimar las pérdidas en la producción lechera

Borges¹,

Mendoza²,

Morais

2019

ARCH ZOOTEC

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Este estudio tuvo como objetivo desarrollar redes neurales artificiales para estimar las pérdidas en la producción lechera, en función del índice de temperatura y humedad (ITU), considerándose el día del año. Accediendo al sitio del Instituto Nacional de Meteorología, se obtuvieron los valores de temperatura y humedad para una serie histórica de 27 años en el municipio de Nova Xavantina-MT. Así, se determinó el índice de temperatura y humedad máximo en cada día del año durante el referido período. Se optó por las redes de múltiples camadas del tipo “perceptron”. Se varió el número de camadas y neuronas para formar diferentes arquitecturas. La proximidad entre los valores reales y los estimados se evaluó por medio de diversos criterios estadísticos y por los coeficientes ofrecidos por el propio programa computacional utilizado. De esta forma se seleccionó la rede con mejor desempeño. Luego se concluyó que las redes presentaron adecuada confiablidad y precisión para predecir el ITU, de acuerdo con el día del año y que ellas pueden ser una herramienta útil en el manejo de vacas lecheras y en el planeamiento de medidas para amenizar el ambiente térmico.

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Simulating climate change impact on soil erosion using RUSLE model − A case study in a watershed of mid-Himalayan landscape

Gupta

Kumar

2017

J Earth Syst Sci

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Climate change, particularly due to the changed precipitation trend, can have a severe impact on soil erosion. The effect is more pronounced on the higher slopes of the Himalayan region. The goal of this study was to estimate the impact of climate change on soil erosion in a watershed of the Himalayan region using RUSLE model. The GCM (general circulation model) derived emission scenarios (HadCM3 A2a and B2a SRES) were used for climate projection. The statistical downscaling model (SDSM) was used to downscale the precipitation for three future periods

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Rainfall erosivity for the State of Rio de Janeiro estimated by artificial neural network

Cited by 6 publications

References 14 publications

Predicting soil erosion using Rusle and NDVI time series from TM Landsat 5

Predicting soil erosion using Rusle and NDVI time series from TM Landsat 5

Redes neurales artificiales para estimar las pérdidas en la producción lechera

Simulating climate change impact on soil erosion using RUSLE model − A case study in a watershed of mid-Himalayan landscape

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