A grey relational model for soil erosion vulnerability assessment in subwatershed of lesser Himalayan region

CATENA ◽  
2022 ◽  
Vol 210 ◽  
pp. 105928
Author(s):  
Shachi Pandey ◽  
Raman Nautiyal ◽  
Parmanand Kumar ◽  
Girish Chandra ◽  
Vijender Pal Panwar
Keyword(s):  
Soil Erosion ◽  
Vulnerability Assessment ◽  
Himalayan Region ◽  
Relational Model ◽  
Grey Relational

Surface Image Edge Detection Algorithm Based on Grey Relational Analysis

2012 ◽  
Vol 461 ◽  
pp. 343-346 ◽  
Author(s):  
Gang Li ◽  
Ying Fang ◽  
Ya La Tong
Keyword(s):  
Edge Detection ◽  
Good Method ◽  
Detection Algorithm ◽  
Relational Model ◽  
Small Data ◽  
The Road ◽  
Image Edge Detection ◽  
Surface Image ◽  
Image Edge ◽  
Grey Relational

Automatic detection of pavement cracks is one of the very hot topics. For the characteristics of “small data, poor information” in the surface image processing, we construct ed a grey image relational model to characterize the local image edge feature, by selecting the appropriate threshold to extract the edge of appropriate level. Finally, simulation experiments show that the new algorithm can effectively improve the road edge detection results, and it is an effective good method worthy further study.

Grey Correlation Model Based on Diatom Indices Evaluating Water Quality of Longjiang River

2013 ◽  
Vol 448-453 ◽  
pp. 43-47 ◽  
Author(s):  
Xin Tang ◽  
De Suo Cai ◽  
Wen Ting Yao
Keyword(s):  
Water Quality ◽  
Water Quality Assessment ◽  
Quality Standard ◽  
Relational Model ◽  
Water Quality Standard ◽  
Diatom Index ◽  
Model Based ◽  
Diatom Indices ◽  
Grey Relational

In order to evaluate the water quality of Longjiang river comprehensively, diatoms of 15 sampling sites are collected to establish the Grey Relational Model based on three diatom indexesIPS, IBD and IDG. From the Cluster analysis of sampling sites and the box plot of diatom index, IBD is the best, then the IPS, the diatom index weights on water quality assessment are 0.637 for IBD, 0.2583 for IPS, 0.1074 for IDG respectively. Finally, by comparing the water quality standard level based on Grey Relational Model, it can be known that the water quality of most sampling sites is fine except that site No.1 and No.3 belong to III water quality standard. The result shows that it is more comprehensive and reasonable to use the Grey Relational Model based on diatom index rather than individual diatom index to assess water quality.

Identifying the factors of China's seasonal retail sales of consumer goods using a data grouping approach–based GRA method

2020 ◽  
Vol 10 (2) ◽  
pp. 125-143
Author(s):  
Zheng-Xin Wang ◽  
Ji-Min Wu ◽  
Chao-Jun Zhou ◽  
Qin Li
Keyword(s):  
Time Series ◽  
Money Supply ◽  
Consumer Goods ◽  
Seasonal Fluctuation ◽  
Relational Model ◽  
Retail Sales ◽  
Content Type ◽  
Data Grouping ◽  
Main Factors ◽  
Grey Relational

PurposeSeasonal fluctuation interference often affects the relational analysis of economic time series. The main purpose of this paper is to propose a new grey relational model for relational analysis of seasonal time series and apply it to identify and eliminate the influence of seasonal fluctuation of retail sales of consumer goods in China.Design/methodology/approachFirst, the whole quarterly time series is divided into four groups by data grouping method. Each group only contains the time series data in the same quarter. Then, the new series of four-quarters are used to establish the grey correlation model and calculate its correlation coefficient. Finally, the correlation degree of factors in each group of data was calculated and sorted to determine its importance.FindingsThe data grouping method can effectively reflect the correlation between time series in different quarters and eliminate the influence of seasonal fluctuation.Practical implicationsIn this paper, the main factors influencing the quarterly fluctuations of retail sales of consumer goods in China are explored by using the grouped grey correlation model. The results show that the main factors are different from quarter to quarter: in the first quarter, the main factors are money supply, tax and per capita disposable income of rural residents. In the second quarter are money supply, fiscal expenditure and tax. In the third quarter are money supply, fiscal expenditure and per capita disposable income of rural residents. In the fourth quarter are money supply, fiscal expenditure and tax.Originality/valueThis paper successfully realizes the application of grey relational model in quarterly time series and extends the applicable scope of grey relational model.

Study on the Competitiveness of Private Express Enterprises in China Based on Grey Relational Model

2016 ◽  
Vol 13 (12) ◽  
pp. 10514-10518
Author(s):  
Zheng Liu ◽  
Yuan-Jun Zhao ◽  
Ping-Ping Zhu ◽  
Si-Jing Chen
Keyword(s):  
Relational Model ◽  
Grey Relational
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