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Application of bayesian network learning methods to land resource evaluation

  • Mountain Environment
  • Published:
Wuhan University Journal of Natural Sciences

Abstract

Bayesian network has a powerful ability for reasoning and semantic representation, which combined with qualitative analysis and quantitative analysis, with prior knowledge and observed data, and provides an effective way to deal with prediction, classification and clustering. Firstly, this paper presented an overview of Bayesian network and its characteristics, and discussed how to lean a Bayesian network structure from given data, and then constructed a Bayesian network model for land resource evaluation with expert knowledge and the dataset. The experimental results based on the test dataset are that evaluation accuracy is 87.5%, and Kappa index is 0.826. All these prove the method is feasible and efficient, and indicate that Bayesian network is a promising approach for land resource evaluation.

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Authors and Affiliations

  1. School of Resource and Environment Engineering, Wuhan University of Technology, 430070, Wuhan, Hubei, China

    Huang Jiejun & He Xiaorong

  2. School of Remote Sensing and Information Engineering, Wuhan University, 430079, Wuhan, Hubei, China

    Wan Youchuan

Authors
  1. Huang Jiejun
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  2. He Xiaorong
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  3. Wan Youchuan
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Corresponding author

Correspondence to Huang Jiejun.

Additional information

Foundation item: Supported by the National Natural Science Foundation of China (60175022, 40571128, 40572166)

Biography: HUANG Jiejun (1976-), male, Ph. D., research direction: spatial information fusion and data mining.

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Jiejun, H., Xiaorong, H. & Youchuan, W. Application of bayesian network learning methods to land resource evaluation. Wuhan Univ. J. Nat. Sci. 11, 1041–1045 (2006). https://doi.org/10.1007/BF02830207

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  • DOI: https://doi.org/10.1007/BF02830207

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