Simulating Soil Water and Solute Transport in a Soil-wheat System Using an Improved Genetic Algorithm

  • Changshou Luo
  • Sufen Sun
  • Junfeng Zhang
  • Qiang Zuo
  • Baoguo Li
Part of the The International Federation for Information Processing book series (IFIPAICT, volume 258)

An improved genetic algorithm was applied and examined to optimize the weights of a neural network model for estimating root length density (RLD) distributions of winter wheat under salinity stress. Thereafter, soil water and solute transport with root-water-uptake in a soil-wheat system were simulated numerically, in which the estimated RLD distributions were incorporated. The results showed that the estimated RLD distributions of winter wheat using the neural network model combined with the improved genetic algorithm, as well as the simulated soil water content and salinity distributions, were comparably well with the experimental data. The method can serve in modeling flow and transport under salinity or saline water irrigated areas.

Keywords

genetic algorithm root length density distribution soil water content salinity simulation 

References

  1. Chen GuoLiang, Wang XuFa, Genetic algorithm and its application, Post & Telecom Press, 1996 (in Chinese)Google Scholar
  2. Homaee M. Root water uptake under non-uniform transient salinity and water stress. Wageningen Agricultural University, the Netherlands, 1999, 41-54, 93-97Google Scholar
  3. Huang XiaoFeng, Pang LiDeng, Chen BiaoHua, Estimating reaction kinetics parameters with an improved real coded genetic algorithm. High Chemical Engineering transactions, 1999, 13 (1):50-55 (in Chinese)Google Scholar
  4. Khosla B K, Gupta R K. Response of wheat to saline irrigation and drainage, Agricultural Water Management, 1997, 32 :285-291CrossRefGoogle Scholar
  5. Luo ChangShou, Application of artificial neural network based on the genetic algorithm in predicting the root distribution. Agricultural University of China, 2000 (in Chinese)Google Scholar
  6. Luo ChangShou, Zhou LiYing. Study on neural network model of improved genetic algorithm. Journal of Information, 2005, 24(5):65-66 (in Chinese)Google Scholar
  7. Thornley JHM, Modelling Shoot: Root Relations: the Only Way Forword? Annal of Botany, 1998, 81:165-171CrossRefGoogle Scholar
  8. Wu J R. Zhang and S. Gui. Modeling soil water movement with water uptake by roots, Plant and Soil, 1999, 215:7-17CrossRefGoogle Scholar
  9. Yuan Zeng, Artificial neural network and its application: Tsinghua University Press, 1999, 66-70 (in Chinese)Google Scholar
  10. Zheng ZhiJun, Zheng ShouQi. Adaptive genetic algorithm based on real-coded evolve neural network. Computer Engineering and Applications, 2000, (9):36-37 (in Chinese)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Changshou Luo
    • 1
  • Sufen Sun
    • 1
  • Junfeng Zhang
    • 1
  • Qiang Zuo
    • 2
  • Baoguo Li
    • 2
  1. 1.Institute of Information on Science and Technology of AgricultureBeijing Academy of Agriculture and forestry SciencesBeijingChina
  2. 2.College of Resources and EnvironmentChina Agricultural UniversityChina

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