Application of neural network and grey relational analysis in ranking the factors affecting runoff and sediment yield under simulated rainfall
Juan Wang A B G , Jun Huang C D , Pute Wu A E F and Xining Zhao E F H
+ Author Affiliations
- Author Affiliations
A College of Water Resources and Architecture Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China.
B School of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou, 225009, Jiangsu, China.
C Pearl River Hydraulic Research Institute, Pearl River Water Resources Commission of the Ministry of Water Resources, 510611, Guangzhou, China.
D Soil and Water Conservation Monitoring Center of Pearl River Basin, Pearl River Hydraulic Research Institute, Pearl River Water Resources Commission of the Ministry of Water Resources, Guangzhou, 510611, Guangdong, China.
E Institute of Soil and Water Conservation, Northwest A & F University, Yangling, 712100, Shaanxi, China.
F Institute of Soil and Water Conservation, Chinese Academy of Sciences & Ministry of Water Resources, Yangling, 712100, Shaanxi, China.
G Department of Land, Air, and Water Resource, University of California, Davis, Davis, CA, 95616, USA.
H Corresponding author. Email: zhaoxn_2014@sohu.com
Soil Research 54(3) 291-301 https://doi.org/10.1071/SR15068
Submitted: 1 March 2015 Accepted: 26 June 2015 Published: 13 April 2016
Abstract
Soil erosion is a dynamic environmental process that influenced by multiple factors. However, most previous studies only examined the causative factors without ranking their relative importance or examining the individual factors. In this work, back-propagation (BP) neural network modelling and grey relational analysis were used to rank the effects of 7 factors—vegetation growth stage (VGS), vegetation type (VT), vegetation cover (VC), rainfall intensity (RI), rainfall duration (RD), antecedent soil moisture (ASM) and slope gradient (SG)—on total runoff (TR) and total sediment (TS) following simulated rainfall events at 5 intensities (30, 45, 60, 90, 120 mm h–1). The experimental plots including 4 treatments, bare soil (control), ryegrass (Lolium perenne L.), purple medic (Medicago sativa L.) and spring wheat (Triticum aestivum L.) under 4 different slopes (9%, 18%, 27.8%, 36.4%). BP models were constructed to predict TR and TS; their predictions tracked the experimental data very closely. A factor analysis based on the BP models ranked the influence of the 7 factors on TR and TS as RI > VC > ASM > RD > VGS > VT > SG and RI > VC > SG > ASM > RD > VGS > VT, respectively. Grey relational analysis provided similar results, ranking the effects of these factors on TR and TS in the order RI > VC > ASM > RD > SG > VGS > VT and RI > VC > SG > ASM > RD > VT > VGS, respectively. These results indicate that runoff and sediment yield depend most strongly on RI and VC, while the effects of the other factors are less pronounced.
Additional keywords: antecedent soil moisture, back-propagation, factor analysis, rainfall simulation, slope gradient, vegetation cover.
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