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[1]ZHAO Peng-yu,XU Xue-xuan.Runoff Simulated with Neural Network under Different Management Patterns in Loess Region[J].Research of Soil and Water Conservation,2012,19(03):227-230.

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Runoff Simulated with Neural Network under Different Management Patterns in Loess Region

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 19 Number of periods: 2012 03 Page number: 227-230 Column: Public date: 2012-06-20

Title:: Runoff Simulated with Neural Network under Different Management Patterns in Loess Region

Author(s):: ZHAO Peng-yu¹, XU Xue-xuan²; 1. Department of Geography, Xinzhou Teachers University, Xinzhou, Shanxi 034000, China;
2. Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi 712100, China

Keywords:: loess region; runoff; neural network; management patterns; simulated rainfall

CLC:: P333.5

DOI:: -

Abstract:: Based on the complexity and nonlinear characteristics of rainfall and slope runoff, a three-layer feed-forward back-propagation (BP) neural network model was constructed and used to simulate the runoff under different management patterns (grass and shrub land, harvested land, and tillage land). In this network model, vegetation coverage, rainfall intensity, gradient, antecedent soil moisture and soil bulk density were selected as the input variables, and runoff intensity under individual rainfall event was the only output variable.The network model was trained and validated by using the measured data obtained from different runoff plots under different rainfall intensity. The results showed that the mean error was less than 10%. The training accuracy and predictive results of tillage land were better than grass and shrub land and harvested land. The comparison for the results of the BP neural network model mothod and regression statistics method showed that the BP neural network model was better to predict the runoff under individula rainfall event.

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