Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Filho, B. Calderano; Polivanov, Helena; Chagas, C. da S.; Júnior, Waldir de Carvalho; Barroso, Emílio Velloso; Guerra, Antônio; Calderano, S. B.

doi:10.1590/s0100-06832014000600003

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…The set of predictor covariables (subsets 1, 2, 3, 4, and CJ40) was used to assess performance by applying four prediction algorithms (J48, REPTree, BFTree, and the Multilayer Perceptron) used in earlier studies (Coelho and Giasson, 2010;Giasson et al, 2011;Sarmento et al, 2012;Arruda et al, 2013;Giasson et al, 2013;ten Caten et al, 2013;Calderano Filho et al, 2014;Dias et al, 2016). These algorithms were selected to compare covariable performance in classifiers with different architecture, the first three with decision tree architecture and the last one with artificial neural networks (ANNs).…”

Section: Methodsmentioning

confidence: 99%

Selection of Environmental Covariates for Classifier Training Applied in Digital Soil Mapping

Campos

Giasson

Costa

et al. 2019

Rev. Bras. Ciênc. Solo

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Section: Methodsmentioning

confidence: 99%

Selection of Environmental Covariates for Classifier Training Applied in Digital Soil Mapping

Campos

Giasson

Costa

et al. 2019

Rev. Bras. Ciênc. Solo

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“…A aplicação de metodologias de inteligência artificial, em particular as Redes Neurais Artificiais (RNA's), vêm se desenvolvendo em diversas áreas da pesquisa científica nas últimas décadas, colocando esta ferramenta como uma importante análise multivariada no estudo de ciências ambientais (Schmidt & Barbosa, 2020) (Coutinho et al, 2016) e, em particular, as ciências do solo (Chagas et al, 2010) (Calderano Filho et al, 2014).…”

Section: Redes Neurais Artificiaisunclassified

“…Para a análise de sistemas cujo banco de dados demanda considerável tempo de processamento computacional, faz-se necessário a utilização de computação de alta performance (Lima et al, 2016) (Braga, 2003). Nesses métodos, a computação em paralelo dos chamados clusters heterogêneos possibilita considerável redução do tempo de processamento.…”

Section: Análise Via Rede Mlp Estrutura Do Cluster Computacionalunclassified

Aplicação De Redes Neurais Artificias Do Tipo Multi Layer Perceptrons (Mlp) Na Proposição De Um Novo Modelo Para Estudo De Resistência De Taludes

2022

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Os eventos pós Mariana e Brumadinho demonstraram à sociedade brasileira a urgência e a necessidade de estudos mais aprofundados quanto a estabilidade de taludes, incluindo grandes barramentos como os utilizados na mineração. O presente trabalho buscou construir um método seguro e mais rápido de obtenção do Fator de Segurança (FS) para garantir a estabilidade de taludes, utilizando redes neurais. Para fins de comparação foi escolhido um método tradicional de cálculo conhecido como Fellenius (1936) para obtenção do Fator de Segurança (FS) para diferentes características de solo e taludes com presença ou não do lençol freático aplicados ao treinamento da rede neural. Construiu-se um cluster em que foi dado aos mesmos parâmetros de entrada sendo: peso específico (KN/m3), coesão (KPa), atrito (0) e talude (L/H) tendo como saída o FS seco. Após o treinamento da rede foram testados 30 conjuntos de dados ao método tradicional e as redes neurais sendo que estas obtiveram 95% de ajuste aos valores obtidos pelo método de Fellenius (1936). Assim, demonstrou-se que as redes neurais, após o aprimoramento e entendimento do método, podem ser utilizadas com inúmeras vantagens em relação ao método de Fellenius tradicional para o cálculo de estabilidade de taludes.

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“…Thirty-one environmental variables (Table 2) were derived from an acquired 10 m spatial resolution digital elevation model (DEM) to create the dataset using SagaGIS software (Conrad et al, 2015). The covariates derived from the DEM are those most commonly used by DSM users (Calderano Filho et al, 2014;Carvalho Junior et al, 2014;Bhering et al, 2016;Chagas et al, 2016;Camera et al, 2017;Gruber et al, 2017;Heung et al, 2017).…”

Section: Environmental Covariatesmentioning

confidence: 99%

Sample design effects on soil unit prediction with machine: randomness, uncertainty, and majority map

Carvalho¹,

Pereira²,

Filho³

et al. 2020

Revista Brasileira De Ciência Do Solo

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Notwithstanding the importance of soil surveys, advances in digital soil mapping have mainly focused on mapping soil attributes or properties rather than developing digital maps of soil units or soil classes. The purpose of this research was to develop digital soil unit maps based on primary soil data collection in areas without previously collected soil information. The covariate variability, the random effect across the data subset and the map outputs were the focuses of this study. We used five datasets with four models (Random Forest -RF, Gradient Boosted Machine -GBM, C5.0, and multinomial log-linear model -MLR). The covariates were grouped into five datasets, where four were grouped by Region Of Interest per Class (ROIC) and one was not grouped by ROIC. To evaluate the random effect to split the dataset, we ran each model 50 times and observed the overall accuracy (OA) and kappa index, and uncertainty, majority and variety maps. The OA of Dataset01 to 04 was lower than to Dataset05 accuracy. However, map outputs of RF and GBM for Dataset01 and Dataset05 had the same majority prediction. It seems that RF and GBM produce consistent results in map outputs according to this methodology and pedologist expertise. To evaluate the uncertainty and the consistency of soil unit prediction, we used the majority maps process. Random Forest, similar to GBM, presented the best results. The increase in the number of covariates was not a guarantee of improvement in the OA or in the quality of the map output. Geographic position and distance raster did not improve the map output according to expert evaluation. Because the variance between the ROICs, when the training and validation datasets were split based on it, the subsets are quite different in relation to the covariates, and this is the reason for the worse results of this model, comparing with the Dataset05. On the other hand, when considering one complete dataset not based on ROICs, the variance of training and validation subsets is lower and produced more accurate parameters of quality.

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Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Cited by 11 publications

References 19 publications

Selection of Environmental Covariates for Classifier Training Applied in Digital Soil Mapping

Selection of Environmental Covariates for Classifier Training Applied in Digital Soil Mapping

Aplicação De Redes Neurais Artificias Do Tipo Multi Layer Perceptrons (Mlp) Na Proposição De Um Novo Modelo Para Estudo De Resistência De Taludes

Sample design effects on soil unit prediction with machine: randomness, uncertainty, and majority map

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