Grey Prediction Model Research Articles

A novel grey model based on Susceptible Infected Recovered Model: A case study of COVD-19

The COVID-19 pandemic has lasted for nearly two years, and the global epidemic situation is still grim and growing. Therefore, it is necessary to make correct predictions about the epidemic to implement appropriate and effective epidemic prevention measures. This paper analyzes the classic Susceptible Infected Recovered Model (SIR) to understand the significance of model characteristics and parameters, and uses the differential and difference information of the grey system to put forward a grey prediction model based on SIR infectious disease model. The Laplace transform is used to calculate the model reduction formula, and finally obtain the modeling steps of the model. It is applied to large and small numerical cases to verify the validity of different orders of magnitude data. Meanwhile, data of different lengths are modeled and predicted to verify the robustness of model. Finally, the new model is compared with three classical grey prediction models. The results show that the model is significantly superior to the comparison model, indicating that the model can effectively predict the COVID-19 epidemic, and is applicable to countries with different population magnitude, can carry out stable and effective simulation and prediction for data of different lengths.

Study of Problems Related to Laying Ballastless Track in the Turnout of Ballasted Track at High-Speed Railway Stations

Due to the complex dynamic interactions of the vehicle-track system in the turnout of high-speed railway stations on a ballasted track, the geometric position of the line changes rapidly. In order to improve the stability of the line, the ballasted track in the turnout area of the stations can be replaced by a ballastless track. Based on the engineering background of laying ballastless track in the turnout area of Zhonglan’s high-speed railway station, this paper studies the subgrade reinforcement scheme, subgrade settlement prediction, and ballasted-ballastless track transition section setting. The result shows that, in order to meet the requirements of laying ballastless track in the turnout area, a reinforcement scheme of subgrade impact rolling, surcharge preloading, and closed water isolation on the subgrade surface is proposed. Based on the commonness and individuality of the grey Verhulst, hyperbola, and Deng Yinger prediction model, this paper optimizes the combination of the three models. The sum of squares of the minimum errors of the combined model is used as the objective function to solve the optimal weighting coefficient, and in this way the combined prediction model is constructed. Through the prediction comparison, we found that the accuracy of the three-model combination prediction model is better than any single- or two-model combination, and its adaptability and reliability are stronger and more reliable. This dynamic analysis model of vehicle tracks was established to carry out the dynamic response analysis under the conditions of fastener stiffness grading transition and ballast glue grading curing ballast bed transition. The results show that the dynamic response indexes under the two transition modes meet the requirements for the smooth running of trains. Due to the convenience of fastener stiffness transition construction and easier replacement, this measure is recommended.

Markov Model-Based Curriculum Association Classification Model and Student Achievement Prediction

With the increasing popularity of higher education and the increasing scale of students in schools, the educational administration management system of various colleges and universities has also accumulated a large amount of performance data. In the face of these massive data, many teaching managers still stay in the simple operation of adding, deleting, modifying, and checking the data and cannot effectively extract and analyze the useful knowledge and information hidden behind the data. Therefore, Markov model is proposed. The advantage of the Markov model is that it has better prediction effect on random series and data series with large volatility; that is, grey prediction model is used to reveal the overall trend of development and change of prediction data series. This paper studies the curriculum association classification model and student achievement prediction based on the Markov model. According to the student’s historical achievement, the average missed detection rate of future grade point is 48.65%, the average missed detection rate of Apriori algorithm is 35.5%, the average missed detection rate of FP growth algorithm is 43.2%, the average missed detection rate of this algorithm is 37.5%, and only 17 of the 40 grade point interval predictions match the actual interval. Make full use of the association rules between courses to provide students with early warning and teacher guidance for specific courses, which can effectively reduce the failure rate while reducing the academic burden. The Markov model can mine students’ data inside and outside the classroom, establish and improve college students’ achievement index system, then deeply analyze and discuss the development of college students’ achievement and ability, and finally give each student’s achievement results.

Grey correlation analysis of milling temperature and milling vibration of TC4 titanium alloy

In this paper, a synchronous acquisition system of milling temperature and milling vibration was established to measure the data of milling temperature and milling vibration. Based on grey correlation theory, the influence of milling parameters and milling vibration on milling temperature was analyzed. It is found that the grey correlation degree between milling temperature and milling parameters and vibration is above 0.70, indicating that milling parameters and vibration have great influence on temperature. In addition, the GM (0, N) grey prediction model of milling temperature on milling parameters and milling vibration was established, and the correlation coefficient of the model was above 0.8, indicating that the reliability of the model was high, and the correlation between milling temperature and milling vibration signal was high.

The optimal allocation and the evaluation of water resources carrying capacity in Shendong mining area

Coal mining has consumed a large amount of water resources in China, and the entire industrial chain of coal production has a larger consumption of water resources than the past especially in Shendong mining area. The water resources carrying capacity determines the scale of coal industry development. The previous research had been conducted at the national, provincial and municipal levels, but there were few studies on the water resources carrying capacity of coal mining areas. The Shendong mining area is located in the coal-rich and water-scarce western region, where precipitation is low and evaporation is high. As a water-scarce area, the development of Shendong mining area is inseparable from the support of coal resources and the rational use of water resources. Therefore, firstly, taking the Shendong mining area as the research object, this paper set high, medium and low scenarios according to the development goals of Shendong mining area, Shenhua Group and the plans of China. The gray forecast model was used to predict the water demand of the economic-social-ecological system of the mining area from 2020 to 2030 under different scenarios. Secondly, a multi-objective optimization model was established with the objective function to optimize the water resources allocation in Shendong mining area. Thirdly, based on the forecast and optimization results, a water resources carrying capacity evaluation model was constructed to evaluate the water resources carrying capacity of the Shendong mining area. Finally, based on the results, the suggestions for rational development and the utilization of water resources in the Shendong mining area were proposed.

New Product Short-Term Demands Forecasting with Boxplot-Based Fractional Grey Prediction Model

The cost of investing in new product development (NPD) is high, and it is a feasible way to use demand forecasts for customer or end-users as a decisive reference. However, this short-term time-series data has difficulties in learning because there is no past performance on which to base the estimates. In the past, it has been proven that the cumulative method of the fractional grey prediction model (FGM) is better than the traditional integer cumulative method of the grey model (GM) model. There are many studies using different optimal algorithms to determine the moderate score order. How to set the coefficient of α in FGM is also worth exploring. Therefore, this research reveals a new fractional grey prediction model which uses box-and-whisker plots to estimate the trends of data, known as the boxplot-based fractional scale prediction model (boxplot-based FGM, BP-FGM) to improve the accuracy of predictors by setting the coefficient sets of α. In the experiment, the examined dataset was collected from a well-known equipment manufacturer as the research object. For modeling, the mean absolute percentage error (MAPE) was established as the objective function of the optimization model, the results from three datasets verified the effect through the commodity attributes and public test data of its production, and the experimental results show that BP-FGM has better prediction results than FGM.

Prediction of Dust Abatement Costs in Construction Demolition Projects

Dust pollution arising out of building demolition has serious health implications on workers, as well as the neighboring communities. Existing research has shown that regulatory and engineering control methods are the most popular for dust pollution control on demolition sites. Though engineering control methods are effective in suppressing dust pollution, they have enormous cost implications for demolition companies. Therefore, accurate prediction of dust treatment costs is an important element of the demolition planning process. However, very little information is available in the existing research about treatment costs. In addition, there has not been any attempt to develop a model which can accurately predict the cost of dust treatment during building demolition. To overcome this knowledge gap, a grey prediction model is built according to the information obtained from twenty previous demolition projects. The historical trend of demolition project cost is combined to establish the prediction model based on GM (1, 1), which can be used to obtain the dust treatment cost of a project with very high accuracy. To further improve the prediction accuracy, this paper also builds a Single Function Residual Identifiability (SFRI) model. The relative error between the actual and predicted dust treatment costs from 2013 to 2021 ranges from 0.003% to 0.077%. Through detailed assessment of various treatment measures using a case study, it was found that the results obtained by the prediction model are very close to the actual costs incurred, which verifies the accuracy of the proposed model.

Petroleum products consumption forecasting based on a new structural auto-adaptive intelligent grey prediction model

The demand for petroleum products has risen rapidly over the last two decades, owing mostly to the residential, transports and industrial sectors. Precise mid and long-term petroleum products consumption prediction is therefore of crucial importance for energy planning and management of strategic reserves. Thus, a new structural auto-adaptive intelligent grey model (SAIGM) is proposed as a solution to this problem. Initially, a novel time response function for predictions is theoretically determined, which addresses the standard grey model's main weaknesses. Subsequently, SAIGM is employed to determine the optimum parameter values to improve the adaptability and flexibility to confront diverse forecasting issues. Altogether, the proposed model offers a triple contribution. Firstly, SAIGM improves the predictive capabilities of intelligent grey models by making it capable of fully extracting the laws of evolution of a system, regardless of time series characteristics. Secondly, structural inflexibility and parametrization issues are addressed, so that SAIGM does not only apply to pure exponential series. Thirdly, the proposed model does not need to preprocess data nor determine the characteristics of input data. Consequently, SAIGM decreases the reliance on modeling knowledge from the standpoint of expert systems. A case study is used to check the feasibility and superiority of SAIGM. Simulation results are compared with recent intelligent grey-based models. The new model appears to benefit from its structural flexibility, since it can generate forecasts with MAPE as low as 1.54% and RMSE of 3.10. Hence, SAIGM outperforms the majority of grey models and intelligent systems known so far

Investigating into the Coupling and Coordination Relationship between Urban Resilience and Urbanization: A Case Study of Hunan Province, China

In the context of accelerated urbanization, constructing resilient cities is an effective approach to tackling risks, such as extreme weather, and various urban challenges. The coupling and coordinated development of urbanization and urban resilience is a prominent embodiment of urban sustainable development and high-quality development capacity. In this study, Hunan Province, China, which is frequently affected by various disasters, is selected as a representative for examining the coupling and coordination relationship between urban resilience and urbanization level. The panel data are adopted to construct a dual-system evaluation framework integrating urban resilience and urbanization level based on the entropy weight-coefficient of variation (CV)-CRITIC method. The coupling coordination degree of this dual-system evaluation framework is calculated with the coupling model in physics and GM (1, 1) grey prediction model. Additionally, the spatial–temporal evolution characteristics of the coupling coordination degree are investigated and analyzed by ArcGIS and Geoda software. The following are indicated from the results: (1) The resilience of all cities is related to their geographical location and is characterized by a decrease from east to west; in addition, the resilience level of most cities presents a downward trend with time. (2) The urbanization level of most cities develops stably with time, but there is a growing gap in the urbanization level between regions. (3) There is a strong correlation between urban resilience and urbanization level in all cities; the unbalanced coupling and coordinated development emerge, specifically manifested by the polarization phenomenon. Eventually, a circle-difference spatial distribution pattern that starts from the central urban agglomeration and gradually decreases to the periphery is formed. (4) The prediction results of the coupling coordination degree suggest that there is an increasingly distinct polarization trend for the coupling and coordinated development between cities, and it is necessary to pay attention to those cities with a declined predicted value. (5) There is a significant positive spatial autocorrelation and agglomeration effects in the distribution of the coupling coordination degree of all cities, and the correlation is getting stronger with each passing year; the correlation mode is mainly characterized by homogeneity and supplemented by heterogeneity. Finally, several suggestions are proposed in this paper, in an attempt to lead the coordinated development of regions by novel urbanization and thus promote the sustainable development of cities. The methods and insights adopted in this study contribute to investigating the relationship between urban resilience and urbanization in China and other regions worldwide.

Unequal-order grey model with the difference information and its application

According to the principle of minimum information, new information priority, and difference information, most existing grey forecast models and their improvement are inconsistent with the grey theory. Therefore, a novel discrete multivariable grey model with unequal fractional-order accumulation is proposed. To improve the accuracy and stability of the model, an optimization algorithm for unequal fractional-order is proposed. The proposed model and algorithm are evaluated with four actual cases. The results show that the novel model has better performance and the proposed unequal fractional-order accumulation operator is better than other existing accumulation operators. Considering the energy consumption, the carbon dioxide emissions in the USA have been forecasted to decrease but remain at a high level by using the novel discrete multivariable grey model. Reducing energy consumption is conducive to reducing carbon dioxide emissions.

Forecasting solar energy consumption using a fractional discrete grey model with time power term

In order to more accurately predict the global solar energy consumption, a new grey prediction model FDGM(1,1, $$t^{\alpha }$$ ) is proposed in this paper. The grey wolf optimizer (GWO) is used to optimize the fractional-order $$r$$ and the time power $$\alpha$$ in the model. The proposed FDGM(1,1, $$t^{\alpha }$$ ) model, two statistical models and other five existing grey models are used to simulate and predict the solar energy consumption in the four economies (France, South Korea, OECD and Asia Pacific region) from 2010 to 2029. The simulation results show that our proposed FDGM(1,1, $$t^{\alpha }$$ ) has higher accuracy than the other seven models. The prediction results based on FDGM(1,1, $$t^{\alpha }$$ ) show that in 2029, the solar energy consumption in South Korea, the OECD and the Asia Pacific region will reach 33,935.32 Ten-trillion J, 1,222,123.45 Ten-trillion J and 2,297,274.45 Ten-trillion J, respectively, but that in France will slowly increase in the next few years and will gradually decrease after reaching a peak of 14,727.34 Ten-trillion J in 2026. The better forecasting solar energy consumption by using the proposed model can provide useful information for the policy-makers to formulate and improve energy policies and measures.

A novel multivariable grey prediction model with different accumulation orders and performance comparison

The uniformity of the accumulation orders of the dependant and independent variables in traditional multivariable grey prediction models ignores the differences in physical properties amongst the variables, which results in unstable or even abnormal model performance. To this end, a new multivariable grey prediction model with dual orders is proposed by differentially defining and optimizing the accumulation orders of the dependant and independent variables. The new model expands the functional structure of the traditional multivariable grey prediction model, improves the preprocessing effect of the accumulation orders based on the original sequences, and enhances the modelling performance of the multivariable grey prediction model. A comparative analysis of three cases verifies that the new model outperforms the other two mainstream multivariable grey prediction models, which confirms that the dual orders of the novel model are reasonable and effective.

A novel grey model with conformable fractional opposite-direction accumulation and its application

To better reflect the principle of new information priority, this paper proposes a new conformable fractional opposite-direction accumulation operator and constructs a new grey prediction model, the CFGNOM (1,1) model. Three cases are used to verify the effectiveness of the CFGNOM (1,1) model, and a grey modeling prediction idea for emergencies is proposed. The CFGNOM (1,1) model is used to predict the per capita primary energy consumption of South and Central America over the next three years. The following conclusions can be drawn. First, the new conformable fractional opposite-direction accumulation method is effective and reasonable and can make full use of the information contained in the latest data to form new sequences. Second, compared with other existing fractional grey models and the BP and SVM models, the CFGNOM (1,1) model has higher prediction accuracy and can make better use of new information for modeling. Third, the application idea of a grey model for emergencies is accurate and can also be applied to the future economic and social prediction affected by the pandemic situation. Fourth, the prediction results show that the per capita energy consumption of South and Central America in 2021 is 49.9–52.7 gigajoules, the per capita energy consumption in 2022 is 48.4–51.8 gigajoules, and the per capita energy consumption in 2023 is 46.8–50.1 gigajoules.

Prediction and analysis of natural gas consumption in chongqing with a grey prediction model group in the context of COVID‐19

In this paper, a grey prediction model group is employed to quantitatively study the impact of COVID‐19 on natural gas consumption in Chongqing, China. First, a grey prediction model group suitable for the prediction of Chongqing's natural gas consumption is introduced, which consists of GM(1,1), TWGM(1,1), and the newly‐developed ODGM(1,1). Then, the model group is constructed to predict Chongqing's natural gas consumption in 2020. Finally, compare the predicted results of the model group with the actual consumption and quantitatively analyze the impact of the epidemic on natural gas in Chongqing. It is found that the impact of the epidemic on the consumption of natural gas in the first quarter of the year is very small, but relatively bigger in the second and third quarters. The study is of positive significance to maintain the supply and demand balance of natural gas consumption in Chongqing in the background of COVID‐19; and it enriches and develops the theoretical system of grey prediction models.

Forecasting carbon emissions using MGM(1,m|λ,γ) model with the similar meteorological condition

Carbon emission is a common concern of the international community and effectively predicting its future trend is necessary for emission reduction planning. Considering that the change trend of carbon emissions is unstable, more attention should be paid to the correction effect of new information on the development trend. Therefore, based on the traditional MGM(1,m) model, this paper introduces the new information priority operator λ and nonlinear parameter γ to strengthen the role of new information, further constructs three comparison models of MGM(1,m|λ), MGM(1,m|γ) and MGM(1,m|λ,γ).Then we apply the new model to the carbon emission prediction of different regions (cities, countries and continents) and different trends (fluctuating, rising and declining). The results illustrate that the new model has higher prediction accuracy, and adding dynamic parameters is a scientific and practical method to improve the forecasting ability of the grey forecasting model. Finally, we analyze the current situation and future development trend of carbon emissions, and put forward reasonable suggestions.

A Study on Livestreaming E-Commerce Development Scale in China Based on Grey System Theory

Grey system theory is an effective mathematical method for studying small sample data and solving poor information problems. The grey correlation model and grey prediction model in this theory have been widely used in scientific studies in various industries. Currently, it is quite difficult for China to formulate scientific policies as livestreaming e-commerce is an emerging industry with little available annual data; therefore, the use of grey system theory is crucial to the study of the future scale of livestreaming e-commerce. In this paper, of many factors influencing the development of livestreaming e-commerce, 14 predictors of livestreaming e-commerce development scale were selected to construct a grey correlation model, by which 5 main predictors were determined. Based on the predictors identified above, 4 grey prediction models of GM (1,1), DGM (1,1), NDGM (1,1), and FDGM (1,1) were constructed, and the accuracy of these models was compared. It was concluded that the NDGM (1,1) model had the best simulation effect. The NDGM (1,1) model is then used to forecast and analyse the indicators of livestreaming e-commerce development scale from 2021 to 2023, and some relevant suggestions were made. This paper applies the new modelling approach to livestreaming e-commerce studies, thus broadening the theoretical study field of livestreaming e-commerce. Moreover, the findings can help the Chinese government make more reasonable and effective decisions as a new study on livestreaming e-commerce was conducted from a different perspective in this paper.

Spatio-Temporal Evolution and Future Simulation of Agricultural Land Use in Xiangxi, Central China

Researches on agricultural land use would help the stakeholders to make better decisions about agricultural resources. However, studies on agricultural land have been lacking. In this context, Xiangxi was chosen as a typical region, and five indicators (Kernel Density, change importance, etc.) and two models (gray forecasting model and GeoSoS-FLUS) were used, to explore the spatio-temporal evolution trends and simulate the future scenarios of agricultural land use. The results were as follows: (1) Xiangxi was dominated by agricultural land, and nearly 50% of total extent was forestry land. Extent of agricultural land decreased by about 56.89 km2 or 3.74% from 2000 to 2018; (2) The density of each agricultural land in the study area had considerable spatial heterogeneity, and showed a main trend of shrinkage, especially in the south regions; (3) In 2030, the spatial pattern and composition of agricultural land in Xiangxi will maintain the existing status, while both of the area and proportion of agricultural land will decline, with a loss of 241.34 km2 or 2.85% decrease from 2000. Nevertheless, the study believed that the slight shrinkage of the agricultural land in Xiangxi is in line with the objective law. At the same time, the study suggested to strengthen the scientific management and rational utilization of agricultural land, with emphasis on arable land and fishery land in the south, especially the administrative center.

Population Prediction of Chinese Prefecture-Level Cities Based on Multiple Models

In recent years, the population growth rate has been gradually declining in China. As the population problem becomes increasingly significant, the accurate prediction of population development trends has become a top priority, used to facilitate national scientific planning and effective decision making. Based on historical data spanning a period of 20 years (1999–2018), this article presents predictions of the populations of 210 prefecture-level cities using the Malthusian model, Unary linear regression model, Logistic model, and Gray prediction model. Furthermore, because the gray prediction model exhibited the highest degree of accuracy in formulating predictions, this study uses the model to predict and analyze future population development trends. The results reveal that the population gap between cities is gradually widening, and the total urban population shows a pattern of rising in middle-tier cities (second-tier cities and third-tier cities) and declining in high-tier cities (first-tier cities and new first-tier cities) and low-tier cities (fourth-tier cities and fifth-tier cities). From the viewpoint of geographical distribution, the population growth rate is basically balanced between the northern part and the southern part of China. In addition, the population growth of the high-tier cities is gradually slowing while the low-tier cities are experiencing a negative growth of population, but middle-tier cities are experiencing skyrocketing population growth. From the viewpoint of regional development, although the development of regional integration has been strengthened over the years, the radiative driving effect of large urban agglomerations and metropolitan areas is relatively limited.

A novel seasonal fractional grey model for predicting electricity demand: A case study of Zhejiang in China

The nonlinear and seasonal fluctuations have brought great challenges to electricity demand prediction. To this end, a novel seasonal intelligent-order grey forecasting model is proposed, which improves the accuracy of traditional grey models with fixed structure Particle swarm optimization is introduced to search the optimal fraction order. Root mean square error, mean absolute percentage error, goodness of fit and Theil’s U are used as performance criteria to test the model superiority. The novel model and other four benchmark models are employed to predict the electricity demand in Zhejiang province. The results show that the proposed model can better capture seasonal variations of electricity demand, the comprehensive percentage mean error is only 2.347%. This grey prediction model with intelligent parameters can be used to provide the basis of seasonal power supply planning to ensure sustainable development of electricity markets.

A Grey BP Neural Network-Based Model for Prediction of Court Decision Service Rate.

The judgment service rate is an important index to reflect the fairness of the judgment of legal cases in a certain area, which is of great significance to verify the accuracy of a court judgment. In this paper, a grey neural network model combining grey system theory and BP neural network algorithm is proposed to predict the index. Analyze the judgment service rate of the court judgment system, and build a prediction system based on the completion rate, completion rate, plaintiff satisfaction, defendant satisfaction, litigation time, property preservation cycle, document delivery time, implementation information disclosure rate, and other key indicators. Through example analysis, it is proved that the combined model of the grey prediction model and BP neural network has a small error and good simulation effect on the prediction of court decision-making service rate, which can better promote the development of court and society.