Pedosphere 17(4): 429--435, 2007
ISSN 1002-0160/CN 32-1315/P
©2007 Soil Science Society of China
Published by Elsevier B.V. and Science Press
| Improving land resource evaluation using fuzzy neural network ensembles |
XUE Yue-Ju1 , HU Yue-Ming2, LIU Shu-Guang3, YANG Jing-Feng1, CHEN Qi-Chang1 and BAO Shi-Tai2 |
1 College of Engineering, South China Agricultural University, Guangzhou 510642 (China). E-mail: xueyj@scdu.edu.cn;
2 College of Information, South China Agricultural University, Guangzhou 510642 (China);
3 U. S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS), Sioux Falls, South Dakota 57198 (USA) |
| ABSTRACT |
| Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evalu- ation, 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, e 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 singIe 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. |
| Key Words: back propagation neural network (BPNN), data types, fuzzy neural network ensembles, land resource evaluation, radial basis function neural network (RBFNN) |
| Citation: Xue, Y. J., Hu, Y. M., Liu, S. G., Yang, J. F., Chen, Q. C. and Bao, S. T. 2007. Improving land resource evaluation using fuzzy neural network ensembles. Pedosphere. 17(4): 429-435. |
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