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Improving land resource evaluation using fuzzy neural network ensembles

Pedosphere

By:
, , , , , and
DOI: 10.1016/S1002-0160(07)60052-6

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Abstract

Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evaluation, these different attributes are usually graded into categorical indexes by land resource experts, and the evaluation results rely heavily on experts' experiences. In order to overcome the shortcoming, we presented a fuzzy neural network ensemble method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values directly. A fuzzy back propagation neural network (BPNN), a fuzzy radial basis function neural network (RBFNN), a fuzzy BPNN ensemble, and a fuzzy RBFNN ensemble were used to evaluate the land resources in Guangdong Province. The evaluation results by using the fuzzy BPNN ensemble and the fuzzy RBFNN ensemble were much better than those by using the single fuzzy BPNN and the single fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN ensemble was lower than that of the single fuzzy BPNN or fuzzy BPNN ensemble, respectively. By using the fuzzy neural network ensembles, the validity of land resource evaluation was improved and reliance on land evaluators' experiences was considerably reduced. ?? 2007 Soil Science Society of China.

Additional publication details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Improving land resource evaluation using fuzzy neural network ensembles
Series title:
Pedosphere
DOI:
10.1016/S1002-0160(07)60052-6
Volume:
17
Issue:
4
Year Published:
2007
Language:
English
Publisher:
Elsevier
Contributing office(s):
Earth Resources Observation and Science (EROS) Center
Description:
7 p.
First page:
429
Last page:
435
Public Comments:
Project supported by the National Natural Science Foundation of China (No. 40671145), the Natural Science Foundation of Guangdong Province (Nos. 04300504 and 05006623), and the Science and Technology Plan Foundation of Guangdong Province (Nos. 2005B20701008, 2005B10101028, and 2004B20701006)
Online Only (Y/N):
N
Additional Online Files (Y/N):
N