Grey Relational Model Research Articles

Study of Global Seawater Corrosivity Classification Based on Marine Environmental Factors Data

ABSTRACTTo evaluate the global seawater corrosivity and ensure the service safety of marine equipment, the methods of seawater corrosivity classification, which contained the standard metal corrosion rates method and the environmental factors evaluation method, are proposed and then applied to the seawater corrosivity classification in typical China seas. By comparing the results from both methods, the feasibility of the environmental factors evaluation method based on the grey relational model is verified. Furthermore, with the collection and processing of typical seawater factors data of global ocean, the corrosivity of global seawater is classified into six grades with the corrosion test data of carbon steel as baseline, and its distribution in different months is visualized by means of ArcGIS technology. The results show that the sea areas with high corrosivity are mainly located at equatorial and tropical sea areas, and for carbon steel, seawater temperature was the main influence on seawater corrosivity.

The measurement of agricultural disaster vulnerability in China and implications for land-supported agricultural resilience building

Climate change has recently induced an increase in natural disasters worldwide, which has affected agriculture, a sector highly sensitive to climatic alterations. The paper measures agricultural disaster vulnerability of China in the period 2000–2020 by establishing vulnerability assessment system, and examines how agricultural infrastructure correlates with agricultural disaster vulnerability by constructing grey relational analysis model. Research results indicate that China's agricultural disaster vulnerability presents an overall decreasing trend during the research period. This is mainly attributed to the decreased disaster exposure and increased adaptability to disaster. Spatially, the agricultural disaster vulnerability in the northeastern, central and southwestern regions of China is higher than other regions. Agricultural transportation and communication infrastructure are found to be more influential to agricultural disaster vulnerability followed by irrigation and power facilities. Implications are proposed to enhance agricultural resilience to climate disasters in China.

Groundwater dynamics clustering and prediction based on grey relational analysis and LSTM model: A case study in Beijing Plain, China

Study regionBeijing Plain, China Study focusThe traditional zoning of groundwater systems relies on lithological characteristics and hydraulic connections. However, the influence of climate change and human activities has led to the emergence of diverse dynamic patterns within these systems. Additionally, the differences in groundwater dynamic characteristics are often ignored when modeling with machine learning methods. In this study, a new clustering method named GRA-CLU was proposed to classify dynamic types of groundwater. Then, regional models are built using LSTM based on the dynamic zoning, aiming to incorporate hydrogeological significance into the pure data-driven model.New hydrological insights for the region: The GRA-CLU method classified the study area into six groundwater dynamic types. Compared with the traditional hydrogeological units, the new zoning result identified two dynamic types that have never been considered: urban influence zone and mountain-plain junction zone where surface water interacts with groundwater. Through analysis, the significant rise of groundwater levels in 2021 was more influenced by heavy rainfall events rather than human activities. This study further explored the performance of LSTM regional models based on the six dynamic zones. The results indicated that the NSE of models increased by 5.7–50.0 %, the improvement was more obvious in zones with irregular fluctuations. The RMSE decreased by 31.5–59.8 %, particularly noticeable in zones with regular annual fluctuations and large samples.

Coupling Coordination Degree of Land, Ecology, and Food and Its Influencing Factors in Henan Province

Studying the spatiotemporal evolution characteristics of the coupling coordination of the land–ecology–food system (LEF) aids in promoting green agricultural development and regional resource management. This study enriches food indicators under the dietary structure and uses the coupling coordination degree model to analyze the coupling coordination relationship among the LEF of 18 cities in Henan Province from 2011 to 2020. The gray relational degree model is used to investigate the main influencing factors determining the synergistic development of the system. The results show that the comprehensive development index of the LEF in Henan Province ranges between 0.4 and 0.6. The overall comprehensive evaluation index of various cities is ranked as Southern Henan > Eastern Henan > Central Henan > Northern Henan > Western Henan, with the greatest fluctuation observed in the food subsystem. During the study period, the coupling degree of Henan’s LEF ranged from 0.277 to 0.996, indicating stages from low- to high-level coupling. The coupling coordination degree ranged from 0.338 to 0.775, generally bordering on imbalance and barely coordinated. The impact of each subsystem evaluation index on the system’s coupling coordination degree can be ranked as food subsystem > ecology subsystem > land subsystem, with the correlation degree of internal indicators of the food and ecology subsystems with the system’s coupling coordination degree being over 85%, emphasizing the importance of strict management. In summary, the coupling coordination of the LEF system in Henan Province urgently needs to be improved; especially, the coordination of the agricultural system is particularly important. Clarifying the spatiotemporal pattern of the LEF coupling and its coordination can provide a scientific basis for the coordinated development of land use, agricultural ecology, and grain production in Henan Province.

Modified Deng’s Grey Relational Analysis Model for Panel Data and Its Applications in Assessing the Water Environment of Poyang Lake

Deng’s grey relational analysis (GRA) model is widely used in clustering because of its simple mathematical mechanisms. For sample data of different dimensions, people have put forward different Deng’s GRA models, including time series data, panel data, and panel time series data. The purpose of this paper is to improve the clustering accuracy of the existing Deng’s GRA model for panel data in order to overcome some of its shortcomings. Firstly, the existing Deng’s GRA model for panel data was tested based on the dataset LP1 of Robot Execution Failures. Then, according to the test results, the existing Deng’s GRA model for panel data is modified by means of Taylor’s formula, and the modified model is successfully validated by the dataset LP1 of Robot Execution Failures. Finally, as a practical application, the modified Deng’s GRA model for panel data is applied to assess the water environment of Poyang Lake over the past five years. Compared with other cluster methods, the results of the case study show that the modified Deng’s GRA model for panel data is applicable and also confirm the remarkable effectiveness of the Chinese government’s water quality regulation in Poyang Lake. Therefore, the modified Deng’s GRA model presented in this paper improves the clustering accuracy compared to the original model and can be applied well to the classification of data with a large dimension.

Prediction of Coal Ash Flow Temperature Based on Gray Relational Analysis, Support Vector Regression and Genetic Algorithm.

Coal ash flow temperature significantly influences the operating conditions of entrained flow bed gasification. The relationship between the flow temperature of coal ash and its chemical composition remains uncertain despite being determined by it. To construct a reliable and accurate predictive method, machine learning models were used, and different support vector regression models were built to predict the flow temperature. The prediction results of the proposed gray relational analysis-genetic algorithm-support vector regression model can achieve high accuracy, with a root-mean-square error of 28.37, mean absolute error of 19.48K, and average deviation of 1.58%. Moreover, the prediction results of the proposed model are more accurate and efficient than those calculated by FactSage software, with a mean absolute error of 93.73K. This demonstrates the viability of applying the proposed machine learning model to predict the flow temperature of coal ash and its promising potential application in the area of coal chemical engineering.

A new failure analysis and validation scheme for shale gas pilot processes

Shale gas pilot processes are usually composed of skid-mounted assemblies of individual equipment, with weak process matching, unknown unit equipment wear, and high failure rates. We propose a solution using the grey relational analysis model, analytical hierarchy process model, and reliability analysis model to achieve failure analysis, prediction, and improvement validation, respectively. The probability and degree of influence relationships between the factors affecting the occurrence of failures are discussed. The practical application of the study area shows that: when the threshold value is set to 0.8833, it can match the grey correlation conclusion well, connect the logic of hierarchical analysis, and cluster the engineering problems; this study area needs to focus on preventing the problems of erosion damages and puncture leakage. Although the short life of the nozzle has a large impact on the overall process, but the frequency of occurrence is not high, the next step needs to continue to collect data, using other mathematical models to mine the fault data internal information.

Retraction Note: Gray relational clustering model for intelligent guided monitoring horizontal wells

Retraction Note: Gray relational clustering model for intelligent guided monitoring horizontal wells

Research on Pricing and Replenishment of Vegetables Based on Grey Relational Model and Seasonal Index Prediction Model

This article mainly studies the problem of automatic pricing and replenishment decisions for vegetable products in supermarkets. A grey correlation model and ARIMA time series model are comprehensively established, and based on clustering analysis and regression analysis methods, the distribution patterns of sales volume for each vegetable category and item over time, as well as the relationship with cost plus pricing and mutual correlation, are analyzed to make reasonable predictions for future sales volume. In order to make decisions on product pricing, sales combination, and replenishment, and maximize supermarket profits, the data is preprocessed through normal test and quartile method. Through screening and sorting analysis, the distribution patterns of sales volume for each vegetable category and item over time are obtained; In order to analyze the relationship between different categories of vegetables and between different single products, a grey correlation model is established to determine the analysis sequence, and the correlation coefficients between each category and each single product are calculated to analyze the mutual relationship. And combined with the hierarchical clustering analysis method, the vegetable categories were divided into three sales types: high, medium, and low, expanding the relationship between categories. At the same time, a periodic chart was drawn to determine the periodicity of sales volume distribution in each category. Then, statistical analysis was conducted on the data to determine the linear correlation between sales volume and cost plus pricing. Based on this, a linear regression model was established to analyze the linear relationship between sales volume and cost plus pricing for the six categories; In order to provide the total daily replenishment amount and pricing strategy for the next week, this article establishes a seasonal index prediction model for sales forecasting. Combining the seasonal distribution of each category, the seasonal index is used to predict the sales volume for the next week. Considering the goal of maximizing profits, the target function is listed to comprehensively analyze the replenishment and pricing strategy for the next week.

Systematic evaluation of integration between China's digital economy and sports industry: Two-stage grey relational analysis and vector autoregressive model.

The development of the digital economy constitutes a key component of China's endeavors to advance towards "Digital China." The sports industry functions as a new catalyst for high-quality economic growth. This study systematically evaluated the integration between these two sectors. First, we conducted two levels of grey relational analysis to assess their integration between 2016 and 2021. Second, we conducted a VAR analysis to determine whether their integration between 2009 and 2021 represents a causal relationship. At the macro level, the grey relational analysis reveals that the sports industry (grade = 0.770) ranked second among China's eight key economic sectors in terms of digital economy integration. At the meso level, a wide variation (ranging from 0.606 to 0.789) existed in the grade of integration between the digital economy and the sub-sectors of the sports industry. According to the VAR model, the digital economy does not Granger cause (p = 0.344) the growth of the sports industry. This study yielded two added values to the existing literature: First, there exists a sectoral imbalance in the digitization process; second, the explosive growth of the sports industry was not primarily caused by the digital economy. Accordingly, the "sports + digital" complex is still in the first wave of technological integration. We propose three policy recommendations, namely, sectoral synergistic development, overtaking via esports IP, and new economy and new regulation. Collectively, these findings provide updated insights for the digital transformation towards "building a leading sports nation" and "Digital China."

Navigating the path to dual carbon goals: Understanding the driving forces of energy transition welfare performance

China's double carbon target aims to improve human well-being and sustainable development. Energy transformation welfare performance (ETWP) is the efficiency of energy transition (ET) in enhancing human well-being. ETWP considers both human well-being and sustainable development. Research on its driving force is helpful in achieving the double carbon goal. Thus, this paper used Logarithmic Mean Divisia Index Model, Fixed Panel Regression Model and Grey Relational Analysis Model to analyze China's ETWP from 2006 to 2022 and predicted ETWP of 31 provinces from 2023 to 2030. The results showed that: (1) ETWP had two rising periods in 2006–2014 and 2015–2022. (2) The government's rationalization policy on energy and environment and technological innovation ability were fundamental driving forces for improving ETWP. (3) There were obvious spatial and temporal distinctions in ETWP, and it would bring out different degrees in most areas. Thus, ET should be promoted by improving the ecological environment and resource utilization efficiency; The importance of the role of scientific and technological innovation and policies should be focused on in promoting ETWP; ET policies based on local developments should be formulated and the energy structure should be changed.

A novel grey relational clustering model under sequential three-way decision framework

In the context of addressing clustering problems with small samples, grey relational clustering (GRC) plays a crucial role. Currently, a three-way GRC has gained popularity, particularly for its ability to handle the uncertain relationship between objects and classes. However, explicit clustering for some objects may not be achievable when employing the one-step decision, which is often a coarse-grained approach. In such cases, re-clustering these objects with a finer granularity becomes necessary. This is where the sequential three-way decision (STWD) proves to be an effective solution. Therefore, this paper is dedicated to designing a novel GRC under the STWD framework, namely GRC-STWD. Specifically, recognizing that some existing threshold calculations in GRCs can be overly subjective and that a pair-wise global threshold may not efficiently leverage available information for accurate object clustering, we introduce a novel threshold algorithm to address these limitations. Furthermore, we propose a conditional probability calculation method combining TOPSIS and grey relational degrees for clustering data lacking decision attributes or class labels. As the development of GRC within portfolio environments remains relatively nascent, we apply the proposed model to portfolio strategy construction. Finally, comparative and experimental analyses validate the model's effectiveness and feasibility. Notably, the developed model serves as a generalization of GRC, contributing to the broader advancement of GRC and STWD methodologies.

Retracted: The Impact of Convention and Exhibition Industry on Regional Economic Development Based on Grey Relational Model

Retracted: The Impact of Convention and Exhibition Industry on Regional Economic Development Based on Grey Relational Model

Retracted: Construction and Application of Physical Education Teacher Literacy Evaluation System Based on Grey Relational Model

Retracted: Construction and Application of Physical Education Teacher Literacy Evaluation System Based on Grey Relational Model

A new grey relational analysis model of cross-sequences

PurposeThe purpose of this paper is to explore a new approach to solve the problem of positive and negative offset in the calculation process of integral elements, then propose a series of new grey relational degree model for cross sequences.Design/methodology/approachThe definitions of cross sequences and area elements have been proposed at first. Then the concept of difference degree between sequences has been put forward. Based on the definition of difference degree between sequences, various modified grey relational degree models for cross sequences have been proposed to solve the measurement problem of cross sequence correlation relationships.Findings(1) The new definition of cross sequences; (2) The area element; (3) Various modified grey relational degree models for cross sequences based on the definition of difference degree between sequences.Practical implicationsThe grey relational analysis model of cross sequences is a difficult problem in grey relational analysis. The new model proposed in this article can effectively avoid the calculation deviation of grey relational analysis model for cross sequences, and reasonably measure the correlation between cross sequences. The new model was used to analyse the food consumer price index in Shaanxi Province, clarifying the relationship between different types of food consumer price indices, some interesting results that are not completely consistent with general economic theory were obtained.Originality/valueThe new definition of cross sequences, the area element and various modified grey relational degree models for cross sequences were proposed.

Development and experiment of an intelligent connected cooperative vehicle infrastructure system based on multiple V2I modes and BWM-IGR method

To increase the efficiency and safety of expressway, this paper constructed a new intelligent connected cooperative vehicle infrastructure system and its effectiveness was verifid from both data and practical applications. Firstly, considering the convenience of using intelligent networking systems for public transportation, a new intelligent connected cooperative vehicle infrastructure system architecture was proposed by incorporating mobile communication methods. Then, the new system was illustrated from road side unit (RSU), on board unit (OBU) and data interaction. Additionally, to verify the effectiveness of the system, this paper proposes a two-stage model named Transformer Embedded Clustering- Hierarchical Density-Based Spatial Clustering of Applications with Noise (TEC-HDBSCAN) model to identify outliers in the trajectory data of vehicles collected by the system and obtain the speed sequence of the vehicle. Finally, data from actual testing scenarios was collected and a Best Worst Method-Improved Gray Relational (BWM-IGR) model was built to verify the effectiveness of the system. The results show that the established intelligent networked transportation system can effectively guide vehicles and collect data with high accuracy.

Charting a sustainable tomorrow: Advancing urban low-carbon economies through comprehensive evaluation and promotion.

With the world population growth, energy consumption and the rapid development of industrial economy, a large amount of carbon emissions has brought destruction and threats to the earth's environment on which human beings depend. The development of low-carbon economy has become the consensus of governments all over the world and has been vigorously advocated & promoted. This paper focuses on the top five global GDP nations in 2022: The United States, China, Japan, Germany, and Britain. A comprehensive evaluation index system of urban low-carbon economic development level is constructed from four dimensions: economic development level, environmental quality, energy consumption emission intensity and social development speed by using literature review and field interview. The evaluation measures are determined using the TOPSIS evaluation method with entropy weight and the grey relational model, providing a comprehensive assessment of the low-carbon economy's development level in these five countries." Judging from the comprehensive evaluation score, the overall development of low-carbon economy in American cities is in good condition and has reached the development standard of low-carbon economy; Germany and Japan rank second and third, and they are low-carbon economies. Britain ranks fourth in comprehensive evaluation, although it belongs to a low-carbon economy country, but there is still a certain gap with Germany and Japan; There is still a big gap between China and the other four countries. Based on the measurement and evaluation outcomes, it presents recommendations and strategies to foster the growth of low-carbon economies, offering valuable insights for the advancement of such economies across different nations. The research results guide countries all over the world to reduce carbon emissions in the process of economic development, protect the earth environment on which human beings depend, and make a better tomorrow for sustainable development.

Predicting Turbidite Channel in Deep-Water Canyon Based on Grey Relational Analysis-Support Vector Machine Model: A Case Study of the Lingshui Depression in Qiongdongnan Basin, South China Sea

Predicting Turbidite Channel in Deep-Water Canyon Based on Grey Relational Analysis-Support Vector Machine Model: A Case Study of the Lingshui Depression in Qiongdongnan Basin, South China Sea

Retracted: Detection of Body Behavior Characteristics in Sports Training Based on Grey Relational Model

Retracted: Detection of Body Behavior Characteristics in Sports Training Based on Grey Relational Model

Analysis of the Relationship between Regional Carbon Financial Market Development and Industrial Structure Upgrading: A Grey Relational Model Approach

Carbon finance, recognized globally as one of the effective solutions to combat global warming, is poised to inject fresh vitality into economic and societal production as it develops. Based on data from 13 cities in the Beijing-Tianjin-Hebei region, this study explores how the development of the carbon finance market affects industrial structure upgrading from the perspectives of carbon finance market development level and industrial structure upgrading. Firstly, by constructing an evaluation indicator system, a comprehensive comparative analysis of the carbon finance market development and industrial structure upgrading of these cities is conducted. Secondly, the study uses the grey relational model to delve into the degree of correlation between the development level of the carbon finance market and the upgrading of the industrial structure. The result shows significant regional disparities among these cities in terms of the development of the carbon finance market and the upgrading of industrial structure. Beijing and Tianjin lead in terms of carbon finance market development. Through the analysis using the grey relational model, it is found that there is a moderate to strong correlation between the level of development of the carbon finance market and industrial structure optimization, indicating the importance of all factors in influencing industrial structure upgrading. Empirical results demonstrate that, on a holistic scale, the development of carbon finance has a significantly positive impact on industrial structure advancement and rationalization. However, disparities exist among different cities, primarily attributed to varying levels of economic development, policy orientation, and developmental stages.