Grey Prediction Model Research Articles

Short‐term estimations of PM10 concentration in the Middle Black Sea region based on grey prediction models

AbstractThe Middle Black Sea region has experienced severe air pollution, with a significant increase in particulate matter (PM) concentration due to a growth in population, financial activity, and an expansion of transportation in recent years. Therefore, the prediction of PM concentration has become a topic of great significance to reduce air pollution and assess the effects on public health. In this study, the grey prediction model (GM (1,1)), the discrete grey model (DGM (1,1)), and the grey Verhulst model (GVM (1,1)) were used to estimate the PM10 concentration of the cities Amasya, Çorum, Ordu, and Samsun in the Middle Black Sea region, for the period from 2022 to 2026. The accuracy of the GM (1,1), DGM (1,1), and GVM (1,1) models in fitting data was calculated using the mean absolute percentage error (MAPE) value. Since three types of prediction models of MAPEs were less than 20%, they were considered a good value for prediction performance. Furthermore, the results showed that the PM10 concentrations of Amasya, Çorum, and Ordu showed a downward trend over the next 5 years. However, the GVM (1,1) model showed an upward trend in the yearly average PM10 concentration in Samsun. In conclusion, these models could be considered a reliable approach in early warning systems for emissions reduction and as a long‐term policy for managing air quality in the Middle Black Sea region.

Prediction of ship trajectory based on deep learning

The rapid development of computer technology strongly promotes the study of maritime traffic safety. The application of artificial intelligence technology makes the ship’s trajectory prediction not limited to complex physical models, and improves the generality of ship’s track prediction. To address the problems of relatively poor prediction accuracy in existing ship trajectory prediction research and high coupling of algorithms during ship navigation while the prediction model needs to be improved, this paper combines grey forecast prediction model and long and short-term memory (LSTM) neural network to establish a neural network model for ship trajectory prediction. This model can effectively increase the number of messages in LSTM model, and reduce the loss value of the model and improve the accuracy of ship track prediction. The prediction method in this paper is verified by AIS data of a ship. The results show that compared with other time series prediction algorithms, this algorithm has higher prediction accuracy.

Coupling Coordination and Spatial Evolution of Sustainable Tourism-Innovation-Environment System: Evidence in China

Coupling tourism development (TD), scientific and technological innovation (ST), and ecological environment (EE) is crucial to regional sustainable development, and it is necessary to explore the spatial and temporal characteristics. The study constructs a coordinated development evaluation index system for the three subsystems. And the spatiotemporal characteristics of the coupling coordination between TD, ST, and EE were revealed by applying the entropy method, coupling coordination model and based on the panel data of 30 regions in China from 2005 to 2020. The results show that: (1) the comprehensive development index and coupling coordination degree show an increasing trend, focusing on the stages of endangered and mild imbalance recession;(2) Most regions are in a state of high coupling and low coordination, mainly concentrated in tourism lagging or science and technology lagging; (3) the coupling coordination shows a spatial pattern as the moderate imbalance in the northwest and the relative balance in the southeast, with a vast regional disparities and clustering characteristics; and (4) the gray prediction model shows that the coupling coordination degree will continue the upward trend in the future. In a nutshell, this research presents a novel framework and results that can be used as an evaluation index and policy insights for coupling TD, ST, and EE.

A novel multivariate time-lag discrete grey model based on action time and intensities for predicting the productions in food industry

The food industry is a pillar industry and plays an important role in the development of China's national economy. In view of the complex environments and the characteristics of fluctuating development in which the Chinese food industry are located, this paper delves into the mechanism of action of relevant factors on the system behavior and proposes a grey prediction model based on action time and intensities considering the widespread existence of time-lag effects that is applicable to the prediction of the food industry. Firstly, considering the action time and intensity of the factors, this paper introduces the action duration, the parameters of time lag and intensity coefficient to expand the multivariate discrete grey prediction model. Secondly, the bidirectional grey A-S (Area-Slope) comprehensive relational model is used to calculate the degree of correlation between relevant factors acting on time-effect sub-sequence and the system behavior sequence under different action durations and time lag, to identify the driving factors and estimate their action durations and time lag. Then, the driving factors are incorporated into the grey prediction model based on action time and intensities, and the structural parameters and intensity coefficients of the model are estimated by using the least square and particle swarm algorithm. Finally, by fitting and predicting two examples in the food industry, the results of the proposed novel model are compared with those of six other prediction models to verify effectiveness. The results demonstrates that the prediction accuracy of the proposed model is better than those of other grey models in time-lag data sequences.

Lithium-ion battery state of health monitoring based on an adaptive variable fractional order multivariate grey model

Accurate assessment of the state of health of lithium-ion batteries using relevant factors is crucial for the maintenance of lithium-ion batteries in electric vehicles. Firstly, data features are extracted from University of Maryland public dataset and dataset is pre-processed. Secondly, the extracted features were analysed using a grey relational analysis model to identify the most significant factors affecting the state of health. Thirdly, this paper proposed an adaptive variable fractional order multivariate grey prediction model to accurately estimate the state of health of lithium-ion batteries. The comparative results demonstrate the overall superiority of the proposed model.

Global Temperature Prediction Study Based On AMIMA, Grey Prediction and Neural Network Models

Global warming has profoundly affected the survival and development of human beings and has become a worldwide problem, and the mitigation of global warming requires a more accurate prediction of global temperature development. In order to accurately predict global temperatures in future years and provide feasible solutions, this paper comprehensively compares the prediction results of three prediction models. Firstly, a time series model is chosen to map the past time series to describe the global temperature changes. In addition, this paper selects three models for analysis by comparing a time series ARIMA model, a grey prediction model and a neural network model, fits the model using data information on the global average temperature for 200 consecutive years, and trains and corrects the model to obtain the projected global temperature for the next 80 years. Considering the advantages and disadvantages of each model and combining the conclusions of previous studies, this paper concludes that the neural network model predicts the most accurate results. Meanwhile, the results indicate that in the future, if the continuous increase in global temperature is not controlled, global warming will continue to cause irreversible damage to the Earth's ecosystem, which in turn will have a negative impact on human health.

Carbon Dioxide Concentration Prediction Study Based on Grey Prediction Model and ARIMA Model

Concentrations of carbon dioxide began to soar after the Industrial Revolution and are now a serious threat to the global climate, and predicting future carbon dioxide levels is essential to balance economic prosperity and sustainable development. In this paper, the gray GM (1, 1) model and the ARIMA model are established to predict future carbon dioxide concentrations. The gray GM (1, 1) model predicts that carbon dioxide concentration levels will reach 685 ppm by 2132, and ARIMA models predict that carbon dioxide concentration levels will reach 685 ppm by 2103. The goodness-of-fit of the gray GM (1, 1) model and the ARIMA model were 0.99989 and 0.99998, respectively, and the ARIMA model was more accurate than the prediction results of the grey prediction model.

Predicting of elderly population structure and density by a novel grey fractional-order model with theta residual optimization: a case study of Shanghai City, China

Background Accurately predicting the future development trend of population aging is conducive to accelerating the development of the elderly care industry. This study constructed a combined optimization grey prediction model to predict the structure and density of elderly population.MethodsIn this paper, a GT-FGM model is proposed, which combines Theta residual optimization with fractional-order accumulation operator. Fractional-order accumulation can effectively weaken the randomness of the original data sequence. Meanwhile, Theta residual optimization can adjust parameter by minimizing the mean absolute error. And the population statistics of Shanghai city from 2006 to 2020 were selected for prediction analysis. By comparing with the other traditional grey prediction methods, three representative error indexes (MAE, MAPE, RMSE) were conducting for error analysis.ResultsCompared with the FGM model, GM (1,1) model, Verhulst model, Logistic model, SES and other classical prediction methods, the GT-FGM model shows significant forecasting advantages, and its multi-step rolling prediction accuracy is superior to other prediction methods. The results show that the elderly population density in nine districts in Shanghai will exceed 0.5 by 2030, among which Huangpu District has the highest elderly population density, reaching 0.6825. There has been a steady increase in the elderly population over the age of 60.ConclusionsThe GT-FGM model can improve the prediction accuracy effectively. The elderly population in Shanghai shows a steady growth trend on the whole, and the differences between districts are obvious. The government should build a modern pension industry system according to the aging degree of the population in each region, and promote the balanced development of each region.

Quantifying the impact of COVID-19 on Chinese ports

PurposeThe purpose of this study is to quantify port efficiency assessment indicators to analyze the impact of COVID-19 on Chinese One Belt One Road (OBOR) ports.Design/methodology/approachThis study utilized a grey prediction model GM(1,1) to forecast five relevant indicators for each of the 17 OBOR ports both with and without COVID-19 background conditions. Additionally, the data envelopment analysis (DEA) efficiency assessment approach was used to analyze the impact of COVID-19 on port efficiency.FindingsThe results indicate that cargo and container throughput growth rates during the COVID-19 pandemic are reduced by 1.7 and 2.1%, respectively. There was also a noticeable reduction in technological efficiency (TE) as well as pure technological efficiency (PTE), while scale efficiency (SE) remained largely unaffected. Furthermore, the dynamic efficiency MI was mainly negatively impacted by changes in overall efficiency change (EFFCH), where pure efficiency change (PECH) less than one contributed significantly towards overall regression of port efficiencies during this period.Originality/valueThis paper is unique in its use of a combination of the grey prediction model and DEA efficiency assessment to quantify changes in important indicators during pandemic periods. This approach not only provides a quantitative understanding of the impact on port-level efficiency through numerical quantification but also offers readers an intuitive understanding.

A novel grey prediction model based on tensor higher-order singular value decomposition and its application in short-term traffic flow

Short-term traffic flow prediction is the key to traffic guidance and control and can directly affect the performance of intelligent transportation systems. Traffic flow data has the characteristics of volatility, chaos, and randomness, which affect the accuracy of the prediction model. Based on the multi-dimensional spatio-temporal data characteristics of traffic flow data, this paper combines the tensor higher-order singular value decomposition theory and the modeling mechanism of the classical grey model GM(1,1) model and establishes the grey GM(1,1) model with tensor higher-order singular values. The tensor higher-order singular value decomposition reflects the periodic, multi-modal, and holistic nature of traffic flow data, which can mitigate the volatility and randomness of traffic flow data and improve the accuracy of the model. Then, the new model is applied to highway short-time traffic flow prediction, analyzing the spatio-temporal nature of traffic flow data, giving the detailed steps of model modeling, and analyzing the correlation between the original traffic flow data and tensor approximation data using grey correlation degrees. There are three cases to illustrate the effectiveness of the model. Case 1 shows that the results of MAPE from nine modeling objects are stable at about 5%, which indicates that the new model has some stability; Case 2 shows that the new model is more adaptable to short-time traffic flow prediction based on the results of three different modeling and prediction objects; Case 3 uses the new model to compare it with two traditional grey forecasting models and two optimization models, and the results indicate that the new model has a total MAPE value of 5.172%, which is better than the other four grey forecasting models. Finally, the new model is applied to short-time traffic flow prediction, and its prediction results are consistent with the trend of the original traffic flow data, indicating that it can reveal the real-time characteristics of the traffic system to a certain extent and provide a reliable theoretical basis for traffic planning, control, and optimization.

Prediction of energy-related carbon emission intensity in China, America, India, Russia, and Japan using a novel self-adaptive grey generalized Verhulst model

Excessive energy-related carbon emission intensity will lead to deterioration of environmental quality and hinder green economic growth. This paper proposes a novel self-adaptive fractional order grey generalized Verhulst model (SAFGGVM) to predict energy-related carbon emission intensity in China, America, India, Russia, and Japan with nonlinear and complex characteristics effectively. Firstly, self-adaptive fractional order and dynamic background value coefficient are introduced to capture the nonlinear evolutionary trends. Subsequently, the optimal nonlinear parameters are determined using Grey Wolf Optimization algorithm through comprehensive comparison. Furthermore, the flexibility of SAFGGVM is verified by presenting its relevance to existing models. Finally, the validity, applicability, and robustness of SAFGGVM are confirmed by comparing with two machine learning models and four grey prediction models. The empirical results exhibit that the overall precision of SAFGGVM model significantly prevails over the others in five cases, with MAPE values less than 6% in both the simulation and prediction intervals. The out-of-sample forecast results indicate that the energy-related carbon emission intensity of five countries will fall into 5.7024, 1.6030, 7.3668, 8.1633, 2.0399 tons/10,000 USD by 2025, and the decreasing rate of developing countries tends to catch up with, or even exceed, developed countries. Projection results can serve as a reference point for countries to achieve green and sustainable development.

RETRACTED ARTICLE: Research on the Prediction of the Inauguration Development Direction of College Students’ Entrepreneurship Education Based on Educational Data Mining

In many related studies, educational data mining technology has been proven to play an important role in predicting the development direction of entrepreneurship education for college students. To further improve the accuracy of the prediction, we chose the grey prediction model as the basic prediction model and automatically optimized the weighting method to improve the model. To solve the problem of predicting the development direction of students’ employment in the guidance of entrepreneurship and employment in colleges and universities, the study selects the grey prediction model as the basic prediction model and chooses the automatic optimization and weighting method to improve the model. Meanwhile, the study establishes a variable system containing six dimensions: academic achievement; physical and mental development; cultural, physical, and artistic quantified status; ideological and political quantified status; scientific and technological innovation quantified status; social work quantified status. The final study used the actual prediction test to analyze the prediction effect. We have selected a variable system consisting of six dimensions, which are the results of extensive research. These dimensions include academic achievement, physical and mental development, cultural/sports/art quantitative status, ideological and political quantitative status, technological innovation quantitative status, and social work quantitative status. Each dimension provides us with important predictions about student entrepreneurship and employment. The results show that the model designed by the survey has only two cases of error in the prediction of 20 actual samples. At the same time, there is no prediction error in the two prediction directions of entrepreneurship and social employment. This shows that the model designed by the study is stable and accurate, and the prediction results are more reliable in the prediction directions of entrepreneurship and social employment. Compared with other relevant research results, our model performs well in predicting accuracy, especially in predicting entrepreneurial and social employment directions, without any prediction errors, indicating that our model has superior performance in predicting stability and accuracy compared to other studies.

Online modeling and prediction of weld bead geometry in robotic gas metal arc based additive manufacturing using grey prediction model

In the directed energy deposition process, online monitoring of weld bead geometry can give real-time information to researchers and manufacturers to understand and control the metal deposition layer by layer. In the current work, an optimization Grey model OGM(1,N)-based online monitoring system was built to monitor the height and depth of metal deposition in each layer. The OGM(1,N) needs fewer training samples with poor information. The training data was updated to improve accuracy in prediction by removing old data and introducing more recent data. The inputs to the OGM(1,N) include the welding time, current, absolute difference in current at regular time intervals (5 s) and arc force. An optimum set of input parameters was identified by calculating the root mean square error (RMSE) for various parameter combinations for the most accurate weld bead height and depth prediction. The interaction of time, current, and arc force significantly affected the height and depth of weld beads. The predicted height and depth of weld beads using the time, current, and arc force showed RMSEs of 2.52 and 0.23, respectively, compared to experimental results. This methodology can assist manufacturers and researchers in understanding and achieving an appropriate balance between process parameters and weld bead height with no training time.

A CRITIC-TOPSIS and optimized nonlinear grey prediction model: A comparative convergence analysis of marine economic resilience

The strength of marine economic resilience (MER) determines whether a region can maintain its original development path or fall into stagnation in the face of economic shock, which is an important starting point for promoting marine economic sustainable development. We discuss the connotation and propose a logical framework for MER, which is estimated by CRITIC-TOPSIS method from 2006 to 2019 based on three dimensions (country, region, province). Furthermore, the temporal evolution trend and regional disparities are accessed by Dagum Gini coefficient. Results reveal that MER has shown a significant upward trend, and it is undergoing gradient change in China, and unfortunately, the specific performance is in the imbalance, and lack of coordination of MER in the southern marine economic circle is the most prominent. Foreseeably, the novel fractional nonlinear grey model with double optimization (DOFNGBM(1,1)) combining Grey wolf optimization (GWO) algorithm is proposed and its performance is examined. Ulteriorly, as predicted by DOFNGBM(1,1) and presented by σ convergence and β convergence models, based on the cross-comparison between in-sample and out-of-sample, significant findings indicate that weak convergence of China’s MER indicates that coastal areas with a low level have faster improvement rates, and MER in China will present different ways of growing delightedly. Comparatively, considering the driving factors of marine economic resilience, all of them are proven to be conducive to raising the steady-state value of MER to a higher level, despite the regional heterogeneity of their effects. Purposefully, it implies that diversified development and synergistic cooperation can improve the endogenous power of marine economic resilience.

Equity and prediction of health resource allocation of traditional Chinese medicine in China.

To analyze the equity of health resource allocation of Traditional Chinese Medicine(TCM) and predict its development during the 14th Five-Year Plan period, and to provide a scientific basis for promoting the improvement of TCM service capacity. The Chinese Mainland (excluding Hong Kong, Macao and Taiwan) was divided into the Northeast, Eastern, Central and Western regions, and the number of TCM medical institutions, the number of TCM beds, practitioners (assistants) of TCM and Chinese pharmacists from 2016 to 2020 were selected as evaluation indicators, and the equity of health resource allocation of TCM was evaluated by Concentration index(CI), Theil index(T) and Health resource agglomeration degree (HRAD), and the development of health resource of TCM during the 14th Five-Year Plan period was predicted by grey prediction model GM (1,1). The Concentration index of the number of TCM medical institutions and TCM beds is negative, and the allocation tends to the regions with low economic development level. The Concentration index of practitioners (assistants) of TCM and Chinese pharmacists is positive, and the allocation tends to the regions with higher economic development level. The number of TCM medical institutions, TCM beds, practitioners (assistants) of TCM and Chinese pharmacists' Theil index allocated by geography is larger than that allocated by population, which indicates that the equity of TCM health resources allocated by population is better than that allocated by geography. The number of TCM medical institutions, practitioners (assistants) of TCM and Chinese pharmacists in between regions by population contributed more than 72% to the Theil index, indicating that the inequity mainly comes from between regions. The number of TCM medical institutions, TCM beds, practitioners (assistants) of TCM and Chinese pharmacists in within regions by geography contributed more than 80% to the Theil index, indicating that the inequity mainly comes from within regions. The HRAD in the Eastern and Central regions is greater than 1, indicating that the equity is better by geography. The HRAD in the Western region is less than 1, indicating insufficient equity by geography. The HRAD/PAD of the Central region (except for the number of TCM beds in 2020) is less than 1, indicating that it cannot meet the medical needs of the agglomerated population. The HRAD/PAD of the Western region (excluding for the Chinese pharmacists) is greater than 1, indicating that the equity is better than that of the agglomeration population. The number of TCM medical institutions and TCM beds tends to regions with low economic development levels, while the number of practitioners (assistants) of TCM and Chinese pharmacists tends to regions with high economic development levels. The equity of the allocation of TCM health resources by population is better than that by geography, and the inequity of the allocation by geography mainly comes from within region. The allocation of health resources of TCM in the four regions is different, and there is a contradiction between equity and actual medical needs.

Novel grey forecasting model with bi-level structure for application to logistic demand

With the increasing demand for Chinese cold chain logistics, accurate forecasting becomes significantly important for decision-making and planning, but the data scarcity is still a challenging problem for modeling method. This paper proposes a novel grey prediction model with bi-level structure to improve the performance of cold chain logistics demand forecasting. The model adapts to the data characteristics and generates corresponding background series to adjust the parameters. A differential evolutionary algorithm is employed to merge the time response functions generated from the bi-level structure. The proposed model extends the applicability compared with existing similar models and can provide higher accuracy for data sequences with a high developing rate. The model outperforms the classic grey model and its extensions in terms of forecasting accuracy. The trend of Chinese cold chain logistics demand from 2021 to 2025 is studied with the proposed model. The computational results show that the proposed grey prediction model is significantly better than other models.

Experimental Study on HMCVT Adaptive Control of Cotton Pickers

Aiming at the stability of the output speed and the poor adaptability of the transmission system during the operation of a cotton picker, a control strategy of hydro-mechanical continuously variable transmission (HMCVT) for cotton pickers based on gray prediction and fuzzy PID is proposed. Firstly, the hardware and software of the existing hydraulic mechanical coupling transmission test-bed of cotton pickers are designed, and the HMCVT human-computer interaction measurement and control system is built by using LABVIEW 2020 software. Then, combined with the transmission theory, the control strategy and gray prediction model are designed. Finally, the continuity test, transmission efficiency test, and adaptive control verification test are carried out. The results show that as the input speed increases, the peak time of the pump motor output speed is prolonged, while the overall speed regulation process is smoother, and the output speed process of the HMCVT system is continuous. As the displacement ratio of the variable pump increases, the transmission efficiency of the hydraulic system increases accordingly, but the highest efficiency is around 0.8. At a working speed of 10 km/h, the transmission efficiency of the HMCVT system of the cotton picker is more than 80%, and the high efficiency of the mechanical system in the whole system makes up for the low efficiency of the hydraulic system, and the transmission efficiency of the cotton picker is the highest at 15–25 km/h. The speed under the adaptive control strategy is better, with good robustness to sudden torque changes and speed fluctuations in the range of ±0.0125% under external load conditions. This study provides a reference for future adaptive control of transmission output speed for heavy-duty vehicles and construction machinery.

Collaborative governance on SO2 pollution from thermal power energy consumption: Evidence from Yangtze River Delta urban agglomeration in China

In order to explore an effective method for the effect of collaborative governance on SO2 pollution from thermal power energy consumption in the Yangtze River Delta (YRD) urban agglomeration, the spatiotemporal evolution of SO2 pollution from thermal power energy consumption was empirically studied by Moran's I first; then the revised Difference-in-Difference-in-Differences model was constructed to examine the effect of collaborative governance on SO2 emission intensity from thermal power energy consumption, and the time effect of collaborative governance was also examined; finally, the Grey System Prediction Model was used to forecast the SO2 emission intensity from thermal power energy consumption in YRD urban agglomeration from 2021 to 2025. The results show that SO2 emissions from thermal power energy consumption in YRD urban agglomeration have positive spatial autocorrelations, and high-high spatial clusters have the tendency of moving to inland cities from coastal cities. Moreover, SO2 pollution from thermal power energy consumption in YRD urban agglomeration has improved year-by-year after the implementation of collaborative governance and the predicted SO2 emission intensity from thermal power energy consumption in YRD urban agglomeration generally shows a downward trend.

Applying the DEMATEL Method to Analyze the Influence of Different Grey Accumulated Generating Operators on Samples

Since the introduction of the grey forecasting model, various improvements have been developed in the field of grey accumulated generating operators (AGOs). Fractional accumulated generating operator (FAGO) and other novel AGOs have enriched the grey theory and expanded its application scope. Nevertheless, limited attention has been given to interrelationships and contributions of new and old information. To fill this research gap, this study employed the DEMATEL method to calculate the influence degree of samples under different grey AGOs. Additionally, the pattern of influence degree variation with respect to the accumulation order was determined. The results demonstrate that, compared to traditional first-order AGO, FAGO and its corresponding grey forecasting models can effectively utilize the advantages of new information by altering the accumulation order.

Forecast combination using grey prediction with fuzzy integral and time-varying weighting in tourism

PurposeTourism demand forecasting is vital for the airline industry and tourism sector. Combination forecasting has the advantage of fusing several forecasts to reduce the risk of inappropriate model selection for analyzing decisions. This paper investigated the effects of a time-varying weighting strategy on the performance of linear and nonlinear forecast combinations in the context of tourism.Design/methodology/approachThis study used grey prediction models, which did not require that the available data satisfy statistical assumptions, to generate forecasts. A quality-control technique was applied to determine when to change the combination weights to generate combined forecasts by using linear and nonlinear methods.FindingsThe empirical results showed that except for when the Choquet fuzzy integral was used, forecast combination with time-varying weights did not significantly outperform that with fixed weights. The Choquet integral with time-varying weights significantly outperformed that with fixed weights for all model combinations, and had a superior forecasting accuracy to those of other combination methods.Practical implicationsThe tourism sector can benefit from the use of the Choquet integral with time-varying weights, by using it to formulate suitable strategies for tourist destinations.Originality/valueCombining forecasts with time-varying weights may improve the accuracy of the predictions. This study investigated incorporating a time-varying weighting strategy into combination forecasting by using CUSUM. The results verified the effectiveness of the time-varying Choquet integral for tourism forecast combination.