[1] Li Baolei, Zhang Yufeng, Shi Xinling, Zhang Kexin, et al. Spatial interpolation method based on integrated RBF neuralnetworks for estimating heavy metals in soil of a mountain region [J]. Journal of Southeast University (English Edition), 2015, 31 (1): 38-45. [doi:10.3969/j.issn.1003-7985.2015.01.007]

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Spatial interpolation method based on integrated RBF neuralnetworks for estimating heavy metals in soil of a mountain region()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

31

Issue:

2015 1

Page:

38-45

Research Field:

Automation

Publishing date:

2015-03-30

Info

Title:

Spatial interpolation method based on integrated RBF neuralnetworks for estimating heavy metals in soil of a mountain region

Author(s):

Li Baolei¹;  Zhang Yufeng¹;  Shi Xinling¹;  Zhang Kexin²;  Zhang Junhua¹

¹School of Information Science and Engineering, Yunnan University, Kunming 650091, China
²Cardiovascular Departments, the Second Affiliated Hospital of Kunming Medical College, Kunming 650031, China

Keywords:

integrated radial basis function artificial neural networks;  spatial interpolation;  soil heavy metals;  mountain region

PACS:

TP183

DOI:

10.3969/j.issn.1003-7985.2015.01.007

Abstract:

A novel spatial interpolation method based on integrated radial basis function artificial neural networks(IRBFANNs)is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at un-sampled sites in a mountain region. The IRBFANNs hybridize the advantages of the artificial neural networks and the neural networks integration approach. Three experimental projects under different sampling densities are carried out to study the performance of the proposed IRBFANNs-based interpolation method. This novel method is compared with six peer spatial interpolation methods based on the root mean square error and visual evaluation of the distribution maps of Mn elements. The experimental results show that the proposed method performs better in accuracy and stability. Moreover, the proposed method can provide more details in the spatial distribution maps than the compared interpolation methods in the cases of sparse sampling density.

References:

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Memo

Memo:

Biographies: Li Baolei(1987—), male, graduate; Zhang Yufeng(corresponding author), male, doctor, professor, yfengzhang@yahoo.com.
Foundation items: The National Natural Science Foundation of China(No.61261007, 61062005), the Key Program of Yunnan Natural Science Foundation(No.2013FA008).
Citation: Li Baolei, Zhang Yufeng, Shi Xinling, et al. Spatial interpolation method based on integrated RBF neural networks for estimating heavy metals in soil of a mountain region[J].Journal of Southeast University(English Edition), 2015, 31(1):38-45.[doi:10.3969/j.issn.1003-7985.2015.01.007]

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