Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/40859
Title: Soil type spatial prediction from Random Forest: different training datasets, transferability, accuracy and uncertainty assessment
Keywords: Digital soil mapping
Soil survey
Legacy data
Mapeamento digital do solo
Levantamento do solo
Dados herdados
Issue Date: May-2019
Publisher: Escola Superior de Agricultura "Luiz de Queiroz"
Citation: MACHADO, D. F. T. et al. Soil type spatial prediction from Random Forest: different training datasets, transferability, accuracy and uncertainty assessment. Scientia Agricola, Piracicaba, v. 76, n. 3, p. 243-254, May/June 2019.
Abstract: Different uses of soil legacy data such as training dataset as well as the selection of soil environmental covariables could drive the accuracy of machine learning techniques. Thus, this study evaluated the ability of the Random Forest algorithm to predict soil classes from different training datasets and extrapolate such information to a similar area. The following training datasets were extracted from legacy data: a) point data composed of 53 soil samples; b) 30 m buffer around the soil samples, and soil map polygons excluding: c) 20 m; and d) 30 m from the boundaries of polygons. These four datasets were submitted to principal component analysis (PCA) to reduce multidimensionality. Each dataset derived a new one. Different combinations of predictor variables were tested. A total of 52 models were evaluated by means of error of models, prediction uncertainty and external validation for overall accuracy and Kappa index. The best result was obtained by reducing the number of predictors with the PCA along with information from the buffer around the points. Although Random Forest has been considered a robust spatial predictor model, it was clear it is sensitive to different strategies of selecting training dataset. Effort was necessary to find the best training dataset for achieving a suitable level of accuracy of spatial prediction. To identify a specific dataset seems to be better than using a great number of variables or a large volume of training data. The efforts made allowed for the accurate acquisition of a mapped area 15.5 times larger than the reference area.
URI: http://repositorio.ufla.br/jspui/handle/1/40859
Appears in Collections:DCS - Artigos publicados em periódicos



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