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	Knowledge discovery from soil maps using inductive learning
	Qi F. ; Zhu A. X.
	2003
关键词	forest ecosystem processes leaf-area index watershed scale extraction networks model
英文摘要	This paper develops a knowledge discovery procedure for extracting knowledge of soil-landscape models from a soil map. It has broad relevance to knowledge discovery from other natural resource maps. The procedure consists of four major steps: data preparation, data preprocessing, pattern extraction, and knowledge consolidation. In order to recover true expert knowledge from the error-prone soil maps, our study pays specific attention to the reduction of representation noise in soil maps. The data preprocessing step has exhibited an important role in obtaining greater accuracy. A specific method for sampling pixels based on modes of environmental histograms has proven to be effective in terms of reducing noise and constructing representative sample sets. Three inductive learning algorithms, the See5 decision tree algorithm, Naive Bayes, and artificial neural network, are investigated for a comparison concerning learning accuracy and result comprehensibility. See5 proves to be an accurate method and produces the most comprehensible results, which are consistent with the rules (expert knowledge) used in producing the soil map. The incorporation of spatial information into the knowledge discovery process is found not only to improve the accuracy of the extracted knowledge, but also to add to the explicitness and extensiveness of the extracted soil-landscape model.
出处	International Journal of Geographical Information Science
卷	17
期	8
页	771-795
收录类别	SCI
语种	英语
ISSN号	1365-8816
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/23202]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Qi F.,Zhu A. X.. Knowledge discovery from soil maps using inductive learning. 2003.

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