Título:	Preprocessing procedures and supervised classification applied to a database of systematic soil survey.
Autoria:	VALADARES, A. P. COELHO, R. M. OLIVEIRA, S. R. de M.
Afiliação:	ALAN PESSOA VALADARES, IAC; RICARDO MARQUES COELHO, IAC; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA.
Ano de publicação:	2019
Referência:	Scientia Agricola, v. 76, n. 5, p. 439-447, Sept./Oct. 2019.
Conteúdo:	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.
Thesagro:	Solo
NAL Thesaurus:	Soil Soil classification
Palavras-chave:	Aprendizado de máquina Pré-processamento Classificação de solos Random forest Machine learning algorithms Tacit soil-landscape relationships Digital soil mapping
Digital Object Identifier:	http://dx.doi.org/10.1590/1678-992X-2017-0171
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPTIA)