Application of neural network and grey relational analysis in ranking the factors affecting runoff and sediment yield under simulated rainfall

Soil Research ◽  
2016 ◽  
Vol 54 (3) ◽  
pp. 291 ◽  
Author(s):  
Juan Wang ◽  
Jun Huang ◽  
Pute Wu ◽  
Xining Zhao
Keyword(s):  
Neural Network ◽  
Sediment Yield ◽  
Grey Relational Analysis ◽  
Vegetation Type ◽  
Simulated Rainfall ◽  
Network Modelling ◽  
Antecedent Soil Moisture ◽  
Relational Analysis ◽  
Grey Relational ◽  
Runoff And Sediment Yield

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.

scholarly journals Soil infiltration based on bp neural network and grey relational analysis

2013 ◽  
Vol 37 (1) ◽  
pp. 97-105 ◽  
Author(s):  
Wang Juan ◽  
Wu Pute ◽  
Zhao Xining
Keyword(s):  
Neural Network ◽  
Rainfall Intensity ◽  
Vegetation Type ◽  
Slope Gradient ◽  
Soil Infiltration ◽  
Antecedent Soil Moisture ◽  
Rainfall Duration ◽  
Relational Analysis ◽  
Cumulative Infiltration ◽  
Grey Relational

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.

Grey Relational Analysis and Neural Network Forecasting of REIT returns

2013 ◽  
Vol 14 (11) ◽  
pp. 2033-2044 ◽  
Author(s):  
Jo-Hui Chen ◽  
Ting-Tzu Chang ◽  
Chao-Rung Ho ◽  
John Francis Diaz
Keyword(s):  
Neural Network ◽  
Grey Relational Analysis ◽  
Relational Analysis ◽  
Grey Relational

A hybrid grey based artificial neural network and C&R tree for project portfolio selection

2017 ◽  
Vol 24 (3) ◽  
pp. 651-665 ◽  
Author(s):  
Farshad Faezy Razi ◽  
Seyed Hooman Shariat
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Decision Tree ◽  
Portfolio Selection ◽  
Grey Relational Analysis ◽  
Project Portfolio ◽  
Content Type ◽  
Relational Analysis ◽  
Project Portfolio Selection ◽  
Grey Relational

Purpose The purpose of this paper is twofold: the selection of project portfolios through hybrid artificial neural network algorithms, feature selection based on grey relational analysis, decision tree and regression; and the identification of the features affecting project portfolio selection using the artificial neural network algorithm, decision tree and regression. The authors also aim to classify the available options using the decision tree algorithm. Design/methodology/approach In order to achieve the research goals, a project-oriented organization was selected and studied. In all, 49 project management indicators were chosen from A Guide to the Project Management Body of Knowledge (PMBOK Guide), and the most important indicators were identified using a feature selection algorithm and decision tree. After the extraction of rules, decision rule-based multi-criteria decision making matrices were produced. Each matrix was ranked through grey relational analysis, similarity to ideal solution method and multi-criteria optimization. Finally, a model for choosing the best ranking method was designed and implemented using the genetic algorithm. To analyze the responses, stability of the classes was investigated. Findings The results showed that projects ranked based on neural network weights by the grey relational analysis method prove to be better options for the selection of a project portfolio. The process of identification of the features affecting project portfolio selection resulted in the following factors: scope management, project charter, project management plan, stakeholders and risk. Originality/value This study presents the most effective features affecting project portfolio selection which is highly impressive in organizational decision making and must be considered seriously. Deploying sensitivity analysis, which is an innovation in such studies, played a constructive role in examining the accuracy and reliability of the proposed models, and it can be firmly argued that the results have had an important role in validating the findings of this study.

Application of grey relational analysis and artificial neural networks on currency exchange-traded notes (ETNs)

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Jo-Hui Chen ◽  
John Francis T. Diaz
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Grey Relational Analysis ◽  
Stock Index ◽  
Forecasting Accuracy ◽  
Relational Analysis ◽  
Volatility Index ◽  
The Us ◽  
Us Dollar ◽  
Grey Relational

AbstractThis study determines which index has the strongest influence on the exchange-trade note (ETN) returns using the grey relational analysis. Results show that the volatility index is the strongest, followed by the S&P 500 stock index, the US dollar index, the CRB index, the Trade index, and the Brent crude oil index. However, the US dollar index has the most significant effect of using the index values of currency ETNs, followed by the S&P 500 stock index, volatility index, Brent crude oil index, the CRB index, and Trade index. This study applies four types of the artificial neural network model, namely, back-propagation neural network (BPN), recurrent neural network (RNN), time-delay recurrent neural network (TDRNN), and radial basis function neural network (RBFNN) to capture the nonlinear tendencies of ETNs for better forecasting accuracy. The paper finds that the RNN and RBFNN models have stronger predictive power among the models, and provides the highest forecasting accuracy for the majority of the currency ETNs. However, the RNN model consistently shows that the low grey relational grades (GRG) variables have the strongest influence on the ETN returns, compared with combining all and high GRG variables. These findings suggest that fund managers and traders can potentially rely on both RNN and RBFNN models, particularly the former, in their applications in financial time-series modeling.

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