Soil infiltration based on bp neural network and grey relational analysis

Revista Brasileira de Ciência do Solo

Wang Juan²  Wu Pute²  Zhao Xining¹ 

[1] ,Northwest A& F University College of Water Resources and Architecture Engineering Yangling Shaanxi ,China

关键词: rainfall intensity;    vegetation cover;    soil permeability;    sensitivity analysis;    intensidade de chuva;    vegetação de cobertura;    permeabilidade do solo;    análise de sensibilidade;   

DOI  :  10.1590/S0100-06832013000100010

来源: SciELO

【 摘 要 】

Soil infiltration is a key link of the natural water cycle process. Studies on soil permeability are conducive for water resources assessment and estimation, runoff regulation and management, soil erosion modeling, nonpoint and point source pollution of farmland, among other aspects. The unequal influence of rainfall duration, rainfall intensity, antecedent soil moisture, vegetation cover, vegetation type, and slope gradient on soil cumulative infiltration was studied under simulated rainfall and different underlying surfaces. We established a six factor-model of soil cumulative infiltration by the improved back propagation (BP)-based artificial neural network algorithm with a momentum term and self-adjusting learning rate. Compared to the multiple nonlinear regression method, the stability and accuracy of the improved BP algorithm was better. Based on the improved BP model, the sensitive index of these six factors on soil cumulative infiltration was investigated. Secondly, the grey relational analysis method was used to individually study grey correlations among these six factors and soil cumulative infiltration. The results of the two methods were very similar. Rainfall duration was the most influential factor, followed by vegetation cover, vegetation type, rainfall intensity and antecedent soil moisture. The effect of slope gradient on soil cumulative infiltration was not significant.

【 授权许可】

CC BY   
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