CLC number: S159-3; P283.8; P283.7
On-line Access:
Received: 2003-10-09
Revision Accepted: 2004-03-22
Crosschecked: 0000-00-00
Cited: 14
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ZHOU Bin, ZHANG Xin-gang, WANG Ren-chao. Automated soil resources mapping based on decision tree and Bayesian predictive modeling[J]. Journal of Zhejiang University Science A, 2004, 5(7): 782-795.
@article{title="Automated soil resources mapping based on decision tree and Bayesian predictive modeling",
author="ZHOU Bin, ZHANG Xin-gang, WANG Ren-chao",
journal="Journal of Zhejiang University Science A",
volume="5",
number="7",
pages="782-795",
year="2004",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2004.0782"
}
%0 Journal Article
%T Automated soil resources mapping based on decision tree and Bayesian predictive modeling
%A ZHOU Bin
%A ZHANG Xin-gang
%A WANG Ren-chao
%J Journal of Zhejiang University SCIENCE A
%V 5
%N 7
%P 782-795
%@ 1869-1951
%D 2004
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2004.0782
TY - JOUR
T1 - Automated soil resources mapping based on decision tree and Bayesian predictive modeling
A1 - ZHOU Bin
A1 - ZHANG Xin-gang
A1 - WANG Ren-chao
J0 - Journal of Zhejiang University Science A
VL - 5
IS - 7
SP - 782
EP - 795
%@ 1869-1951
Y1 - 2004
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2004.0782
Abstract: This article presents two approaches for automated building of knowledge bases of soil resources mapping. These methods used decision tree and bayesian predictive modeling, respectively to generate knowledge from training data. With these methods, building a knowledge base for automated soil mapping is easier than using the conventional knowledge acquisition approach. The knowledge bases built by these two methods were used by the knowledge classifier for soil type classification of the Longyou area, Zhejiang Province, China using TM bi-temporal imageries and GIS data. To evaluate the performance of the resultant knowledge bases, the classification results were compared to existing soil map based on field survey. The accuracy assessment and analysis of the resultant soil maps suggested that the knowledge bases built by these two methods were of good quality for mapping distribution model of soil classes over the study area.
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