Advertisement

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

  • Liu TingXiang
  • Zhang ShuWen
  • Wu QuanYuan
  • Bao WenDong
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (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.

Keywords

Agricultural land classification BP Neural Network Genetic Algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chen, M.Q., Zhao, X.M., Chen, A.Z., Cai, Z.M., Zhang, R.C.: Grade- Determination of Arable Land Based on GIS—A Case of Linhai City, Zhejiang Province. Acta Agriculturae Universitis Jiangxiensis 21(3), 421–424 (1999)Google Scholar
  2. 2.
    Peng, J., Jiang, Y.J., Liu, S., Zhang, Q.C.: Research progress and prospect on classification and grading of agricultural land in China. Chinese Journal of Eco-Agriculture 13(4), 167–170 (2005)Google Scholar
  3. 3.
    Li, Y.X., Wang, J.M., Zhang, M.L., Zhang, Y.R.: Compare and application research of agricultural land classifaction and gradation methods. China Land Science 11(1), 33–37 (1997)MathSciNetGoogle Scholar
  4. 4.
    Song, J.B., Zhou, S.L., Peng, B.Z.: Study On Agricultural Land Gradation In Metropolitan Suburban—A Case Study In Tianhe District Of Guangzhou City. Soils 36(2), 168–172 (2004)Google Scholar
  5. 5.
    Yun, W.J.: Agricultural Land Classification and Its Application. China Agricultural University 6 (2005)Google Scholar
  6. 6.
    Cybenko, G.: Approximation by superposition of a sigmoidal function. Mathematics of Control, Signals, Systems 2(4), 303–314 (1989)MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Chen, G.L., Wang, X.F., Zhuang, Z.Q., Wang, D.S.: Genetic Algorithm And Its Application, pp. 23–76. Posts & Telecom Press, Beijing (1996)Google Scholar
  8. 8.
    Bao, W.D.: Study on Land Use Dynamic Change Based on GIS. Shandong University of Science and Technology 6 (2007)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Liu TingXiang
    • 1
    • 2
  • Zhang ShuWen
    • 1
  • Wu QuanYuan
    • 3
  • Bao WenDong
    • 4
  1. 1.Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchunChina
  2. 2.Graducate SchoolChinese Academy of SciencesBeijingChina
  3. 3.College of Population, Resources and EnvironmentShandong Normal UniversityJinanChina
  4. 4.Shandong Land Surveying and Planning InstituteJinanChina

Personalised recommendations