Runoff Simulated by Neural Network Under Different Landuses on the Loess Area
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    Abstract:

    Based on the complex nonlinear characteristics of rainfall-runoff on slope,a three-layer feed-forward back-propagation network model for runoff in different landuses(grass and shrubby slope,cutting slope and plowing slope) is established.Structure of the model has five input variables,including vegetation coverage,rainfall intensity,gradient,antecedent soil moisture content,and soil bulk density and one output variable of runoff amount from single rainfall event.The network model is validated by using the data observed from field simulated rainfall experiment.The BP network model is compared with conventional method as well.Results show that the BP network model may improve the precision of forecast.

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赵鹏宇,徐学选,王玉,史新合,廖鑫,李波.黄土区不同土地利用方式下径流量的神经网络模拟[J].水土保持通报英文版,2008,(5):144-147

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History
  • Received:January 01,2008
  • Revised:June 11,2008
  • Adopted:
  • Online: November 26,2014
  • Published: