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Comparing Supervised and Unsupervised Artificial Neural Network for Conducting Eco-Environment Vulnerability Assessment

Zhiyuan Yao, Wei Qi, Xiao-ping Pang, Lan Tao, Comparing Supervised and Unsupervised Artificial Neural Network for Conducting Eco-Environment Vulnerability Assessment. In: Proceeding of Annual International Conference onComputer Science Education: Innovation and Technology (CSEIT 2011), 2010.

Abstract:

The present study adopts an unsupervised neural network, i.e. the SOM-Ward clustering to
conduct eco-environmental vulnerability assessment. The 797 assessment units on Fildes
Peninsula are clustered based on the ten assessment indexes; the results effectively
display the eco-environment vulnerability classifications on Fildes Peninsula and the factors
that impact them. In addition, the results of the SOM-Ward clustering are compared with
those predicted by the back propagation (BP) neural network, further verifying the
practicability and reliability of the SOM-Ward model. Based on the results of both models,
we summarize the characteristics of the eco-environment vulnerability of the Antarctic ice-
free areas, providing further materials for the study of Antarctic eco-environmental.

BibTeX entry:

@INPROCEEDINGS{inpYaQiPaTa10a,
  title = {Comparing Supervised and Unsupervised Artificial Neural Network for Conducting Eco-Environment Vulnerability Assessment},
  booktitle = {Proceeding of Annual International Conference onComputer Science Education: Innovation and Technology (CSEIT 2011)},
  author = {Yao, Zhiyuan and Qi, Wei and Pang, Xiao-ping and Tao, Lan},
  year = {2010},
  keywords = {Eco-environmental vulnerability assessment; artificial neural network (ANN); Self-organizaingmap (SOM); Ward’s clustering, the back propagation (BP)neural network; GIS data processing},
}

Belongs to TUCS Research Unit(s): Data Mining and Knowledge Management Laboratory

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