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Research of Agricultural Land Classification and Evaluation Based on Genetic Algorithm Optimized Neural Network Model

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Software Engineering and Knowledge Engineering: Theory and Practice

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 115))

Abstract

The common method used for agricultural land classification is weighted factors sum. Classification results are greatly influenced by subjective weights. Besides, the relationship between relevant factors and agricultural land grades would not be linear. This paper constructs the genetic algorithm optimized neural network model to calculate agricultural land physical quality value nonlinearly. This model is applied to agricultural land classification and evaluation work in Licheng district of Jinan city. It is prove to be effective and robust.

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References

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Author information

Authors and Affiliations

  1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130012, China

    Liu TingXiang & Zhang ShuWen

  2. Graducate School, Chinese Academy of Sciences, Beijing, 100039, China

    Liu TingXiang

  3. College of Population, Resources and Environment, Shandong Normal University, Jinan, 250014, China

    Wu QuanYuan

  4. Shandong Land Surveying and Planning Institute, Jinan, 250014, China

    Bao WenDong

Authors
  1. Liu TingXiang
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  2. Zhang ShuWen
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  3. Wu QuanYuan
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  4. Bao WenDong
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Corresponding author

Correspondence to Liu TingXiang .

Editor information

Editors and Affiliations

  1. Central China Normal University, Lvting Yajing, Hongshan Qu 10-3-102, Wuhan, 430079, China, People's Republic

    Yanwen Wu

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© 2012 Springer-Verlag GmbH Berlin Heidelberg

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TingXiang, L., ShuWen, Z., QuanYuan, W., WenDong, B. (2012). Research of Agricultural Land Classification and Evaluation Based on Genetic Algorithm Optimized Neural Network Model. In: Wu, Y. (eds) Software Engineering and Knowledge Engineering: Theory and Practice. Advances in Intelligent and Soft Computing, vol 115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25349-2_62

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  • DOI: https://doi.org/10.1007/978-3-642-25349-2_62

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25348-5

  • Online ISBN: 978-3-642-25349-2

  • eBook Packages: EngineeringEngineering (R0)

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