Application of grey correlation analysis for evaluating the artificial lake site in Pingtung Plain, Taiwan

The overuse of groundwater in coastal areas of Taiwan has caused serious land subsidence, seawater intrusion, and soil salinification. Artificial lakes are one of the alternatives that increase groundwater water resources. The Pingtung Plain is one of the most bountiful groundwater resource areas in Taiwan. The grey correlation method was used to evaluate the four optimal sites in the Pingtung Plain. Evaluations were based on hydraulic conductivity, the amount and quality of source water, the distance between the potential artificial lake site and the source water intake position, and the quantity and quality of gravel dug from the potential artificial lake site. Based on groundwater recharge and water storage, the grey correlation method showed that site D (Dar-Shiang-Yung) was the best choice.Key words: artificial lake, grey correlation analysis, groundwater recharge, Pingtung Plain.

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A Hybrid Approach Based on Grey Correlation Analysis and Fuzzy Comprehensive Judgment for Evaluating Service Quality of Passenger Train

Advances in Mechanical Engineering ◽

10.1155/2014/195496 ◽

2014 ◽

Vol 7 (1) ◽

pp. 195496 ◽

Cited By ~ 2

Author(s):

Wanxiang Wang ◽

Qi Zhao ◽

Ruijun Guo

Keyword(s):

Correlation Analysis ◽

Service Quality ◽

Hybrid Approach ◽

Passenger Train ◽

Grey Correlation ◽

Grey Correlation Analysis ◽

Comprehensive Judgment

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Exploring Options for Improving Potato Productivity through Reducing Crop Yield Gap in Loess Plateau of China Based on Grey Correlation Analysis

Sustainability ◽

10.3390/su11205621 ◽

2019 ◽

Vol 11 (20) ◽

pp. 5621 ◽

Cited By ~ 2

Author(s):

Ning Wang ◽

Yingying Xing ◽

Xiukang Wang

Keyword(s):

Soil Moisture ◽

Correlation Analysis ◽

Correlation Method ◽

Potato Yield ◽

High Yield ◽

Available Phosphorus ◽

Yield Gap ◽

Grey Correlation ◽

Experimental Station ◽

Grey Correlation Analysis

Differences in crop yield between different fields in the same region have existed for a long time. Methods for improving the productivity of low-yield fields are a hot topic in large-scale agriculture. This experiment was carried out in potato planting farmland and at a potato experimental station in the Loess Plateau in China to study the effects of soil moisture and soil nutrients on potato yield potential and yield gap. The relationships between potato yield and soil nutrient factors were analyzed using the grey correlation method. The grey correlation method is a new technique for performing prediction, relational analysis and decision-making in many areas. The results indicate that (1) the high-yield group at the potato experimental station (HE) was 72,678 kg/ha; the mean-yield group at the potato experimental station (ME) was 36,083 kg/ha; the high-yield group in the potato planting farmland (HF) was 34,259 kg/ha; and the mean-yield group in the potato planting farmland (MF) was 19,386 kg/ha. (2) The yield gap (YG1) between HF and MF was 14,873 kg/ha; the yield gap (YG2) between ME and the MF was 16,697 kg/ha; the yield gap (YG3) between HE and the MF was 53291 kg/ha. (3) The effects of soil moisture and nutrients on potato yield were ranked from large to small: soil available potassium content > soil nitrate nitrogen content > soil organic matter content > soil water content > soil available phosphorus content. The results of correlation analysis and grey correlation analysis showed that the available potassium had the strongest correlation with potato tuber yield. (4) The content of nitrate nitrogen was significantly correlated with the content of available potassium and available phosphorus, while the water content was significantly correlated with the content of organic matter. According to the influence of soil moisture and nutrients on the potato tuber yield, it is suggested that integrated water and fertilizer cultivation measures be implemented, and the input of potash fertilizer and nitrogen fertilizer be increased.

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Teaching Evaluation Algorithm Based on Grey Relational Analysis

Complexity ◽

10.1155/2021/5596518 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Xiaoying Zhang ◽

Xiuying Yang ◽

Jing Yang

Keyword(s):

Correlation Analysis ◽

Undergraduate Education ◽

Reference Data ◽

Influence Factor ◽

Data Matrix ◽

Data Sequence ◽

Control Data ◽

Grey Correlation ◽

Grey Correlation Analysis

Grey correlation analysis uses grey correlations to describe the strength, magnitude, and order of relationships among factors. The data sequence of the drmined data is used as a reference data string, and the data sequence matrix of each influence factor is used as a control data matrix to calculate the correlation between the data sequence and the reference of each factor in the control data matrix. The basic idea of quantitatively measuring the correlation between each factor and the object is to determine the similarity between the reference data sequence and the shape of multiple comparison data sequences and to determine whether the connection is strong. It reflects the degree of correlation between curves. In this paper, we examine the problem of evaluating the quality of undergraduate education by multiple indicators and establish a comprehensive evaluation model of the quality of undergraduate education in the 13 prefecture level cities by using the grey correlation analysis. The correlation coefficient of each index was obtained by the grey correlation analysis, the correlation number of each index was analyzed and ranked, and finally the improvement of the number of “advanced teachers” and the education of 13 prefectures were reduced as much as possible.

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