decision trees for digital soil mapping on subtropical basaltic steeplands Árvores de decisão para o mapeamento digital de solos em encostas basálticas subtropicais

Clicks: 165
ID: 244226
2011
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Abstract
When soil surveys are not available for land use planning activities, digital soil mapping techniques can be of assistance. Soil surveyors can process spatial information faster, to assist in the execution of traditional soil survey or predict the occurrence of soil classes across landscapes. Decision tree techniques were evaluated as tools for predicting the ocurrence of soil classes in basaltic steeplands in South Brazil. Several combinations of types of decicion tree algorithms and number of elements on terminal nodes of trees were compared using soil maps with both original and simplified legends. In general, decision tree analysis was useful for predicting occurrence of soil mapping units. Decision trees with fewer elements on terminal nodes yield higher accuracies, and legend simplification (aggregation) reduced the precision of predictions. Algorithm J48 had better performance than BF Tree, RepTree, Random Tree, and Simple Chart.
Quando levantamentos de solos não estão disponíveis para atividades de planejamento de uso das terras, técnicas de mapeamento digital de solos podem ser úteis. Mapeadores de solos podem processar as informações espaciais rapidamente, auxiliando na execução de levantamentos de solos tradicionais ou prevendo a ocorrência de classes de solos na paisagem. Avaliaram-se técnicas de análise de decisão na predição da ocorrência de classes de solos em áreas de encostas basálticas no Sul do Brasil. Várias combinações de tipos de algoritmos de árvore de decisão e quantidade de elementos nos nós terminais das árvores de decisão foram testadas usando mapas de solos com a legenda original e com legenda simplificada. Em geral, o uso de árvores de decisão foi eficaz na predição de ocorrência de unidades de mapeamento de solos. Menor número de elementos no nó terminal das árvores de decisão produziu acurácias mais altas e a simplificação da legenda (agregação) reduziu a precisão das predições. O algoritmo J48 teve melhor desempenho que BF Tree, RepTree, Random Tree, e Simple Chart.
Reference Key
giasson2011scientiadecision Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Elvio Giasson;Eliana Casco Sarmento;Eliseu Weber;Carlos Alberto Flores;Heinrich Hasenack
Journal journal of the iranian chemical society
Year 2011
DOI
10.1590/S0103-90162011000200006
URL
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162011000200006
Keywords
gis

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