Grey Prediction Model Research Articles

Grey fuzzy prediction model of soil organic matter content using hyper-spectral data

PurposeIn order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.Design/methodology/approachBased on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.FindingsThe results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R2 = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.Practical implicationsThe research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.Originality/valueThe paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.

A New Conformable Fractional-Order Time-Delay Grey Bernoulli Model with the Arithmetic Optimization Algorithm and Its Application in Rural Regional Economy

To further promote the development of the grey system theory, this paper develops a novel conformable fractional-order grey Bernoulli model with a time-delay effect, namely, the CFTDNGBM (1, 1) model. In addition, the arithmetic optimization algorithm (AOA) is incorporated into the system of the model to solve the hyperparameters existing in the model. Compared with the previous grey prediction models, the CFTDNGBM (1, 1) model with a conformable fractional-order accumulation operation (CFAO), time-delay factor, and Bernoulli parameter has stronger compatibility in structure. The proposed model and its nine competitive models with excellent performance are used to predict and analyze the consumption level and per capita consumption expenditure of rural residents in China to verify the feasibility of the proposed method. The case results show that in both cases, the seven descriptive indicators of the CFTDNGBM (1, 1) model are higher than those of its competing models. Therefore, the CFTDNGBM (1, 1) model has a certain application value.

A novel grey prediction model with structure variability and real domain fractional order and its performance comparisons

PurposeThis paper aims to overcome the problem that the single structure of the driving term of the grey prediction model is not adapted to the complexity and diversity of the actual modeling objects, which leads to poor modeling results.Design/methodology/approachFirstly, the nonlinear law between the raw data and time point is fully mined by expanding the nonlinear term and the range of order. Secondly, through the synchronous optimization of model structure and parameter, the dynamic adjustment of the model with the change of the modeled object is realized. Finally, the objective optimization of nonlinear driving term and cumulative order of the model is realized by particle swarm optimization PSO algorithm.FindingsThe model can achieve strong compatibility with multiple existing models through parameter transformation. The synchronous optimization of model structure and parameter has a significant improvement over the single optimization method. The new model has a wide range of applications and strong modeling capabilities.Originality/valueA novel grey prediction model with structure variability and optimizing parameter synchronization is proposed.HighlightsThe highlights of the paper are as follows:A new grey prediction model with a unified nonlinear structure is proposed.The new model can be fully compatible with multiple traditional grey models.The new model solves the defect of poor adaptability of the traditional grey models.The parameters of the new model are optimized by PSO algorithm.Cases verify that the new model outperforms other models significantly.

An ecological environment impact assessment of municipal solid waste based on grey prediction model

Aiming at the problem of low accuracy in the impact assessment of urban waste on ecological environment, a method of urban waste ecological environment impact assessment based on grey prediction model is proposed. First, the pollution type of urban waste is determined, and the important pollution data of urban waste is extracted with the help of the calculation of comprehensive water quality identification index. Then, the total ranking of pollution data is determined by the square root method, the judgement matrix is set to calculate the weight of pollution data, and the urban waste ecological environment pollution index system is constructed. Lastly, with the help of grey prediction model, the positive correlation between urban waste and ecological environment is determined, and the urban waste ecological environment impact assessment algorithm is designed to complete the impact assessment. The results show that the evaluation accuracy can be effectively improved by using the evaluation method in this paper.

Analysis and Forecast of Cross-border E-commerce Scale in Guangdong Province Based on Grey Model

With the advent of the era of big data, data analysis and cloud computing have become important tools for analysis and prediction. With the rapid development of the Internet, Guangdong Province, as a big province opening to the outside world, the analysis and prediction of cross-border e-commerce based on the grey model is an important way and basis to improve the business in Guangdong Province. Firstly, the important factor is calculated from affecting scale factors cross-border e-commerce by the grey correlation degree. Further, based on the factor, a grey prediction model is established to predict the cross-border e-commerce development in Guangdong Province. The results show that the local financial allocation for science and technology is an important factor affecting cross-border e-commerce development in Guangdong Province. Moreover, the grey prediction model with a relative error of 8.2% proves that the impact will continue to grow in the future.

Green GDP Calculation Study Based on CVM and EWM

This paper examines the calculation method of green GDP and how it determines climate change. Firstly, a new method of calculating green GDP is proposed by combining the coefficient of variation method and entropy weight method to calculate the weight of the selected indicators on green GDP, which can be effectively combined with the development situation of each country for judgment, and the results of green GDP calculation of several countries are given to study the influence of different indicators on economy and environment. Then, using annual CO2 emissions as a reference, the amount of change in future GDP and green GDP is firstly predicted by grey forecast model, and then future CO2 emissions are calculated by support vector regression, which can reflect the future climate change. The results suggest that CO2 emissions are likely to continue to rise in the future.

Study of Long-Term Energy Storage System Capacity Configuration Based on Improved Grey Forecasting Model

Distributed generation equipment improves renewable energy utilization and economic benefits through an energy storage system (ESS). However, dominated by short-term data, the configuration of long-period ESS capacity is absent based on the dynamic change of load, which leads to a large deviation from the expected return. Considering the system characteristics of lack of data and less information, after introducing the grey theory, we propose a new long-term capacity configuration method for ESS and establish the long-term grey forecasting model (GFM) of user load, improving the basic forecasting model to improve the accuracy of the long-term forecasting model. Then, the scheduling model is established with the maximum economic and social benefits as the optimization objective. Based on the forecast data of the improved grey forecasting model (IGFM), the hierarchical solution method is used to solve the scheduling model. Finally, the parameters are configured based on the service life of the equipment and the expected rate of return. The simulation results show that higher accuracy is realized in the improved prediction model, and the improved algorithm gets higher convergence speed and precision. Apart from that, the nonlinear correlation trend of the EES return rate between the capacity and life cycle is revealed. Compared with the ESS configuration in a short period, this study provides more comprehensive and accurate data support for the capacity configuration of the ESS, reducing the error between the actual return and the expected return significantly.

基于GM(1,1)灰色预测模型的山东省城乡居民基本养老保险参保趋势预测

基于GM(1,1)灰色预测模型的山东省城乡居民基本养老保险参保趋势预测

Research on Wordle Based on Time Series Model and Grey Prediction Model

Research on Wordle Based on Time Series Model and Grey Prediction Model

Research on Vegetable Incoming and Pricing Strategies Based on Support Vector Machines and Gray Prediction Models

Research on Vegetable Incoming and Pricing Strategies Based on Support Vector Machines and Gray Prediction Models

Study on the coupling and coordinated development of population, economy, technology and environment —Take Zhongyuan urban agglomeration as an example

In order to explore the internal relationship of the population - economy - science and technology - environment complex system, by constructing the evaluation index system of the population - economy - science and technology - environment complex system of the Central Plains urban agglomeration, using the combination weighting method of game theory to weight the indicators, and establishing an improved coupling coordination model, the development level of the population - economy - science and technology - environment of Zhongyuan urban agglomeration in 2010-2019 was analyzed, At the same time, the PSO optimized fractional grey prediction model is used to predict the period from 2020 to 2024.

Research on whether global temperature warms and its influencing factors based on grey predictions

In order to study whether the global temperature warms and the biggest factors affecting temperature change, this paper collects the global average temperature from 1900 to 2022, sets up an experimental group and a control group to train three types of grey prediction models to select the optimal model, selects the metabolic type and analyzes the error, and uses the least squares method to fill in the difference to achieve future temperature prediction, and judges whether the future temperature warms by comparing the century temperature growth rate. Then, the least squares method, multiple linear regression and comprehensive evaluation were used to analyze the influence between natural and regional factors and temperature change, and the influence factors of natural and human factors on temperature change were calculated by grey correlation to determine the maximum influencing factors. The results show that the temperature growth rate in the 20th century is 1.0412, and the temperature growth rate in the 21st century is 1.0958 higher than that of 0.0546 in the 20th century, so the global temperature will warm in the future; the biggest factor affecting global warming is "forest area", with an impact coefficient of 0.9376.

Forecasting China's natural gas external dependence under the "Dual Carbon" goals by a new grey model.

As a low-carbon and cost-effective clean energy source, natural gas plays an important role in achieving China's "Dual Carbon" target. In this article, a new three-parameter discrete grey prediction model is used to simulate and forecast the production and consumption of natural gas in China from the perspective of background value optimization. Then the minimum mean absolute percentage error as the objective function from the perspective of fractional order cumulative generation in the real number field. Last, a fractional order in the real number field three parameter discrete grey prediction model TDGM(1,1,z,r(R)) is constructed under the condition of optimal background value. Then we use the model to simulate and predict China's Natural Gas External Dependence (NGED) under the "Dual Carbon" target. The results show that the performance of the new model is better than that of the traditional model GM(1,1) and DGM(1,1), thus proving the practicability and effectiveness of the new model. Put forward relevant policy suggestions according to the prediction results of China's NGED, and provide decision-making reference for the Chinese government to achieve the "Dual Carbon" goals.

Optimized Grey Prediction Model Based on Multiple Background Projection and Sequence Decomposition

Optimized Grey Prediction Model Based on Multiple Background Projection and Sequence Decomposition

Dynamic Response of Shrubbery Fire to Meteorological Changes in Yunnan Province

This paper takes shrubland fires as the research object and selects typical shrubland forest fire cases that occurred throughout Yunnan Province from 1954 to 2018 as the research samples, and the shrubland fire loss elements and meteorological data are studied by SPSS correlation analysis and MATLAB gray prediction model to analyze shrubland fires and meteorological elements. The results showed that there were significant correlations between shrubland fires and meteorological data; the shrubland fire over fire area, forest damage area, and economic loss composite data index showed positive correlations with annual maximum temperature, annual average minimum temperature, annual average temperature, and annual sunshine hours; the annual average relative humidity, precipitation days, and the composite index of the same day meteorological data showed significant negative correlations, etc. In exploring the main meteorological factors affecting the distribution of forest fires, we establish a meteorological fire model to provide a real and credible scientific basis and theoretical foundation for future forest fire prevention and prediction.

上海市养老从业人员供给趋势预测与建议——基于灰色预测模型GM(1,1)

上海市养老从业人员供给趋势预测与建议——基于灰色预测模型GM(1,1)

The Prediction of Steel Bar Corrosion Based on BP Neural Networks or Multivariable Gray Models.

The corrosion of steel bars in concrete has a significant impact on the durability of constructed structures. Based on the gray relational analysis (GRA) of the accelerated corrosion data and practical engineering data using MATLAB, a back propagation neural network (BPNN) model, a multivariable gray prediction model (GM (1, N)), and an optimization multivariable gray prediction model (OGM (1, N)) of steel corrosion were established by using a sequence of the key affecting factors. By comparing the prediction results of the three models, it is found that the GM (1, N) model has larger fitting and prediction errors for steel corrosion, while the OGM (1, N) model has smaller prediction errors in the accelerated corrosion data; the BPNN model offers more accurate predictions of the practical engineering data. The results show that the BPNN and OGM (1, N) models are all suitable for the prediction of steel bar corrosion in concrete structures.

Statistical Analysis on Economic Development in China

With the rapid growth of China's economy, all walks of life have developed healthily and stably. The relationship between education, finance and economy is becoming closer and closer. It is urgent to investigate the relationship between the three to achieve the right remedy. It is urgent to investigate the relationship between the three to achieve right remedy. The study of the relationship between education, finance and economy has certain representativeness and practical significance. Firstly, using Pearson correlation coefficient analysis method and copula entropy, this paper selects the index data of the three subsystems of education. Firstly, using Pearson correlation coefficient analysis method and copula entropy, this paper selects the index data of the three subsystems of education, finance and economy in Hubei Province from 2005 to 2020, and discusses the correlation degree between education, finance and economy in detail. Secondly, it constructs the coupling coordination degree model of the three subsystems of education, finance and economy, obtains the coordination degree and Secondly , it constructs the coupling coordination degree model of the three subsystems of education, finance and economy, obtains the coordination degree and coordinated development level of the three subsystems, and constructs the GM (1,1) grey-prediction model to predict the coupling coordination degree in The results show that there is a strong correlation between education and finance, education and economy, finance and economy. It is predicted that it will continue It is predicted that it will continue the previous slow upward trend from 2021 to 2024, and initially reach the stage of good coordinated development in 2024. In order to further promote the coordinated development of regional education, finance and economy in China and provide reference for the construction of domestic education finance In order to further promote the coordinated development of regional education, finance and economy in China and provide reference for the construction of domestic education finance economy system, this paper puts forward reasonable suggestions: starting from the internal governance of the two systems, promote the benign This paper puts forward reasonable suggestions: starting from the internal governance of the two systems, promote the benign development of the three systems; Increase investment in education and pay attention to the quality of education and teaching; Accelerate the paper puts forward reasonable suggestions: starting from the internal governance of the two systems, promote the benign development of the three systems; Increase investment in education and pay attention to the quality of education and teaching; accelerate the optimization and upgrading of education structure and economic structure; Strengthen financial supervision and improve the participation of The Government of the Republic of Korea will continue to promote the development of the three systems; Increase investment in education and pay attention to the quality of education and teaching; Accelerate the optimization and upgrading of education structure and economic structure; Strengthen financial supervision and improve the participation of investors in the financial market.

The Impact of Economic Growth, Renewable Energy, Non-renewable Energy and Trade Openness on the Ecological Footprint and Forecasting in Turkiye: an Case of the ARDL and NMGM Forecasting Model

In this study, the effects of economic growth, renewable and non-renewable energy production and trade openness on ecological footprint for Turkey were investigated. By using the annual data for the period 1980-2016, the short- and long-term relationship with the Autoregressive Distributed Lag Model (ARDL) was examined. In addition, a prediction model is presented with the Multivariate Gray Prediction Model (NMGM) method. According to the findings obtained from the ARDL model, economic growth, renewable and non-renewable energy production have a positive effect of 0.166, 0.1431 and 0.1118, respectively, on the ecological footprint in the long run. In the short run, economic growth, renewable energy production and non-renewable energy production has the same effect of 0.1941, 0.1673 and 0.1308 on the ecological footprint. In addition, no effect of trade openness on the ecological footprint has been detected, both in the long and short run. The originality of this study is to investigate the short- and long-term effects of economic growth and trade openness on the ecological footprint, in addition to the amount of renewable energy production and non-renewable energy production in Turkey, using the ARDL model. In addition, another originality of this study is a dynamic evaluation of the ecological footprint for Turkey and the determination of the impact values of the variables that affect the ecological footprint. ARIMA models, in which the dependent variable is estimated with its own past values, are generally used as estimation models. Likewise, univariate gray estimation models also make estimations with the dependent variable's own past values. Another unique aspect of this study is the use of a gray estimation model, in which the variables that have been shown to have a significant short- and long-term relationship with ARDL are also included in the model.

Spatiotemporal evolution analysis and prediction of drought in Henan Province based on standardized precipitation evapotranspiration index

Abstract Drought is one of the major natural disasters in the world, and it is important to understand and grasp the spatiotemporal evolution characteristics of drought for drought monitoring and prevention. In this study, the standardized precipitation evapotranspiration index (SPEI) was calculated based on monthly precipitation and average temperature data from 15 meteorological stations in Henan Province, China during the period 1987–2020. The spatiotemporal evolution characteristics of droughts in Henan Province were analyzed using run theory and Moran's I, and drought prediction was carried out using grey disaster theory. The results show that: (1) During the period 1987–2020, the overall drought in Henan Province shows a slow intensification trend, and SPEI-12 has a certain adaptability in the drought characterization of Henan Province; (2) The frequency of droughts in the northern and eastern regions of Henan Province is high, the southern region is prone to continuous drought, the drought ephemeris and intensity show a strong positive correlation, and the spatial spillover effect of autumn drought is the most obvious; (3) The grey disaster prediction model has excellent accuracy and predicts that the next possible drought in Henan Province will be in the east of Henan Province in the spring of 2023.