Data/hora: 09/06/2024 02:01:52

Provedor de dados: | 63
País: | Brazil
Título: | Preprocessing procedures and supervised classification applied to a database of systematic soil survey
Autores: | Valadares,Alan Pessoa; Coelho,Ricardo Marques; Oliveira,Stanley Robson de Medeiros.
Data: | 2019-10-01
Ano: | 2019
Palavras-chave: | Machine learning algorithms; Random forest; Tacit soil-landscape relationships; Digital soil mapping.
Resumo: | ABSTRACT: Data Mining techniques play an important role in the prediction of soil spatial distribution in systematic soil surveying, though existing methodologies still lack standardization and a full understanding of their capabilities. The aim of this work was to evaluate the performance of preprocessing procedures and supervised classification approaches for predicting map units from 1:100,000-scale conventional semi-detailed soil surveys. Sheets of the Brazilian National Cartographic System on the 1:50,000 scale, “Dois Córregos” (“Brotas” 1:100,000-scale sheet), “São Pedro” and “Laras” (“Piracicaba” 1:100,000-scale sheet) were used for developing models. Soil map information and predictive environmental covariates for the dataset were obtained from the semi-detailed soil survey of the state of São Paulo, from the Brazilian Institute of Geography and Statistics (IBGE) 1:50,000-scale topographic sheets and from the 1:750,000-scale geological map of the state of São Paulo. The target variable was a soil map unit of four types: local “soil unit” name and soil class at three hierarchical levels of the Brazilian System of Soil Classification (SiBCS). Different data preprocessing treatments and four algorithms all having different approaches were also tested. Results showed that composite soil map units were not adequate for the machine learning process. Class balance did not contribute to improving the performance of classifiers. Accuracy values of 78 % and a Kappa index of 0.67 were obtained after preprocessing procedures with Random Forest, the algorithm that performed best. Information from conventional map units of semi-detailed (4th order) 1:100,000 soil survey generated models with values for accuracy, precision, sensitivity, specificity and Kappa indexes that support their use in programs for systematic soil surveying.
Tipo: | Info:eu-repo/semantics/article
Idioma: | Inglês
Identificador: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162019001500439
Editor: | São Paulo - Escola Superior de Agricultura "Luiz de Queiroz"
Relação: | 10.1590/1678-992x-2017-0171
Formato: | text/html
Fonte: | Scientia Agricola  v.76 n.5 2019
Fonte: | info:eu-repo/semantics/openAccess