Dynamic Analysis and Prediction of Ecological Footprint in Jilin Province of China Based on Grey Prediction Model

2014 ◽  
Vol 472 ◽  
pp. 899-903 ◽  
Author(s):  
Biao Gao ◽  
Qing Tao Xu
Keyword(s):  
Prediction Model ◽  
Ecological Footprint ◽  
Jilin Province ◽  
Grey Prediction ◽  
Dynamic Changes ◽  
Ecological Capacity ◽  
Grey Prediction Model ◽  
Ecological Deficit ◽  
Gray Prediction ◽  
Per Capita

The paper calculates ecological footprint per capita and ecological capacity per capita in the Jilin province during 1998 and 2010 by using the ecological footprint theory, and analyzes the dynamic changes of ecological footprint per capita and ecological capacity per capita, and obtains development prediction model of ecological footprint per capita and ecological capacity per capita based on grey prediction model. The results indicate the ecological footprint per capita had increased continuously from 1.7841 hm2 per capita to 3.2013 hm2 per capita between 1998 and 2010. During this period, ecological capacity per capita dropped from 1.3535 hm2 per capita to 1.3028 hm2 per capita. Ecological deficit had increased from 0.4306 hm2 per capita to 1.8985 hm2 per capita that showed that the development of Jilin province was in an unsustainable status. The gray prediction model shows the ecological footprint per capita in the Jilin province will increase from 3.4833 hm2 per capita to 5.7022 hm2 per capita between 2011 and 2020, ecological capacity per capita will drop from 1.2978 hm2 per capita to 1.2676 hm2 per capita and ecological deficit will increase from 2.1855 hm2 per capita to 4.4346 hm2 per capita.

The application of trigonometric grey prediction model to average per capita natural gas consumption of households in China

2019 ◽  
Vol 9 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Qiuping Wang ◽  
Subing Liu ◽  
Haixia Yan
Keyword(s):  
Natural Gas ◽  
Prediction Model ◽  
Forecasting Model ◽  
Grey Prediction ◽  
Content Type ◽  
Grey Prediction Model ◽  
Grey Forecasting ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Per Capita

Purpose Due to high efficiency and low carbon of natural gas, the consumption of natural gas is increasing rapidly, and the prediction of natural gas consumption has become the focus. The purpose of this paper is to employ a prediction technique by combining grey prediction model and trigonometric residual modification for predicting average per capita natural gas consumption of households in China. Design/methodology/approach The GM(1,1) model is utilised to obtain the tendency term, then the generalised trigonometric model is used to catch the periodic phenomenon from the residual data of GM(1,1) model for improving predicting accuracy. Findings The case verified the view of Xie and Liu: “When the value of a is less, DGM model and GM(1,1) model can substitute each other.” The combination of the GM(1,1) and the trigonometric residual modification technique can observably improve the predicting accuracy of average per capita natural gas consumption of households in China. The mean absolute percentage errors of GM(1,1) model, DGM(1,1), unbiased grey forecasting model, and TGM model in ex post testing stage (from 2013 to 2015) are 32.5510, 33.5985, 36.9980, and 5.2996 per cent, respectively. The TGM model is suitable for the prediction of average per capita natural gas consumption of households in China. Practical implications According to the historical data of average per capita natural gas consumption of households in China, the authors construct GM(1,1) model, DGM(1,1) model, unbiased grey forecasting model, and GM(1,1) model with trigonometric residual modification. The accuracy of TGM is the best. TGM helps to improve the accuracy of GM(1,1). Originality/value This paper gives a successful practical application of grey model GM(1,1) with the trigonometric residual modification, where the cyclic variations exist in the residual series. The case demonstrates the effectiveness of trigonometric grey prediction model, which is helpful to understand the modeling mechanism of trigonometric grey prediction model.

Application of Improved Grey Prediction Model in Jilin Province of the Software Industry

2014 ◽  
Vol 998-999 ◽  
pp. 1079-1082 ◽  
Author(s):  
Wei Shi Yin ◽  
Pin Chao Meng ◽  
Yan Zhong Li
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Historical Data ◽  
Software Industry ◽  
Experimental Results ◽  
Jilin Province ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Better Than

Based on the modified grey prediction model, the outputs of software industry in Jilin Province were predicted. First the historical data and updated the data were pre-treated by iteration. Then it was found that the results from the modified grey prediction model were better than that from traditional grey prediction model by residual analysis. Finally, the prediction about the outputs of software industry in Jilin Province was given for the next five years. According to the experimental results, our proposed new method obviously can improve the prediction accuracy of the original grey model.

Application of Multivariable Grey Prediction

2010 ◽  
Vol 44-47 ◽  
pp. 2717-2723
Author(s):  
Ling Ling Li ◽  
Jun Jie Han ◽  
Meng Wu ◽  
Zhi Gang Li
Keyword(s):  
Prediction Model ◽  
Multivariable Analysis ◽  
Single Variable ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Gray Prediction ◽  
Relevant Variables ◽  
Multiple Variables ◽  
System Point

In this paper, a multivariable analysis of grey prediction model MGM (1,n) is proposed ,which is based on single-variable gray prediction model GM (1,1). For multiple variables of a system, they influence each other and interrelate, MGM (1,n) model is description of each of the major relevant variables in the system from system point of view.Through comparison of an example of forecasting relay failure, can be obtained: multivariable grey prediction is more accurate and more close to the actual value than the single variable prediction.

Preliminary Exploration and Analysis of Agro-Ecological Footprint Calculation in Huanggang

2012 ◽  
Vol 573-574 ◽  
pp. 456-460
Author(s):  
Miao Tian ◽  
Min Zhou
Keyword(s):  
Sustainable Development ◽  
Ecological Footprint ◽  
Quantitative Method ◽  
Study Region ◽  
The Sustainable Development ◽  
Ecological Capacity ◽  
Ecological Deficit ◽  
Per Capita ◽  
The Impact ◽  
Agriculture Development

The ecological footprint is a quantitative method which can measure the sustainable development of ecological. In this way, we can conclude the impact degree of human activities on the environment. In this paper, the study region is Huanggang which is in Hubei province. Based on the introduction of ecological footprint, we calculate and analyze the agro-ecological footprint of Huanggang.The results show that the agro-ecological footprint of Huanggang is: 1.728252hm2/person.The available ecological capacity is 0.314946hm2/person; Per capita ecological deficit is up to1.413306hm2/person.This result shows that the agriculture development of Huanggang is in the state of unsustainable, meanwhile, we proposed some countermeasures to improve the agriculture sustainable capability of Huanggang.

Study on Sustainable Use of Land Based on Ecological Footprint Model: a Case of Shenzhen

2013 ◽  
Vol 295-298 ◽  
pp. 2551-2556
Author(s):  
Guo Liang Ou ◽  
Shui Kui Tan
Keyword(s):  
Sustainable Development ◽  
Ecological Footprint ◽  
Sustainable Use ◽  
Calculation Formula ◽  
The Sustainable Development ◽  
Ecological Capacity ◽  
Footprint Model ◽  
Ecological Deficit ◽  
Per Capita ◽  
Reasonable Use

Reasonable use of land or not, directly related to the sustainable development of a country or region. This paper introduced the basic concept, calculation formula and method of the ecological footprint. We calculated the ecological footprint of Shenzhen by application of the ecological footprint model. The results showed that the per capita ecological footprint in Shenzhen in 2011 was approximately 2.486 hm2, while the per capita ecological capacity was approximately 0.0597 hm2, the per capita ecological deficit was approximately -2.433 hm2, and the ecological footprint is about 47.33 times greater than the ecological capacity. Finally, we discussed the limitations of applying the ecological footprint model to judge the sustainable use of land in this paper.

scholarly journals Research on Prediction Accuracy of Coal Mine Gas Emission Based on Grey Prediction Model

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1147
Author(s):  
Jun Zeng ◽  
Qinsheng Li
Keyword(s):  
Prediction Model ◽  
Coal Mine ◽  
Prediction Error ◽  
Prediction Accuracy ◽  
Coal Mines ◽  
Grey Prediction ◽  
Gas Emission ◽  
Grey Prediction Model ◽  
Gray Prediction ◽  
Working Face

In order to achieve the accuracy of gas emission prediction for different workplaces in coal mines, three coal mining workings and four intake and return air roadway of working face in Nantun coal mine were selected for the study. A prediction model of gas emission volume based on the grey prediction model GM (1,1) was established. By comparing the predicted and actual values of gas emission rate at different working face locations, the prediction error of the gray prediction model was calculated, and the applicability and accuracy of the gray prediction method in the prediction of gas gushing out from working faces in coal mines were determined. The results show that the maximum error between the predicted and actual measured values of the gray model is 2.41%, and the minimum value is only 0.07%. There is no significant prediction error over a larger time scale; the overall prediction accuracy is high. It achieves the purpose of accurately predicting the amount of gas gushing from the working face within a short period of time. Consequently, the grey prediction model is of great significance in ensuring the safety production of coal mine working face and promote the safety management of coal mine.

scholarly journals Forecasting the Number of New Coronavirus Infections Us-ing an Improved Grey Prediction Model

2021 ◽  
Author(s):  
Hui Li ◽  
Bo Zeng ◽  
Jianzhou Wang ◽  
Hua’an Wu
Keyword(s):  
Prediction Model ◽  
Particle Swarm Optimization Algorithm ◽  
Percentage Error ◽  
Small Data ◽  
Grey Prediction ◽  
Matlab Simulation ◽  
Short Term ◽  
New Model ◽  
Grey Prediction Model ◽  
Coronavirus Infections

Background: Recently, a new coronavirus has been rapidly spreading from Wuhan, China. Forecasting the number of infections scientifically and effectively is of great significance to the allocation of medical resources and the improvement of rescue efficiency. Methods: The number of new coronavirus infections was characterized by “small data, poor information” in the short term. The grey prediction model provides an effective method to study the prediction problem of “small data, poor information”. Based on the order optimization of NHGM(1,1,k), this paper uses particle swarm optimization algorithm to optimize the background value, and obtains a new improved grey prediction model called GM(1,1|r,c,u). Results: Through MATLAB simulation, the comprehensive percentage error of GM(1,1|r,c,u), NHGM(1,1,k), UGM(1,1), DGM(1,1) are 2.4440%, 11.7372%, 11.6882% and 59.9265% respectively, so the new model has the best prediction performance. The new coronavirus infections was predicted by the new model. Conclusion: The number of new coronavirus infections in China increased continuously in the next two weeks, and the final infections was nearly 100 thousand. Based on the prediction results, this paper puts for-ward specific suggestions.

Research on Method of Metro Monitoring Model Based on Grey Prediction Algorithm

2014 ◽  
Vol 548-549 ◽  
pp. 641-645
Author(s):  
Mao Hua Liu ◽  
Xiu Bo Sun
Keyword(s):  
Prediction Model ◽  
Safety Monitoring ◽  
Prediction Algorithm ◽  
Grey Prediction ◽  
Monitoring Point ◽  
Value Analysis ◽  
Grey Prediction Model ◽  
Monitoring Model ◽  
Surface Monitoring ◽  
Set Up

Grey prediction model is a model to predict the trend maturely, its application in the subway safety monitoring is of great significance. Set up by MATLAB software to complete the grey prediction model, and take the surface monitoring point for example, Comparing the prediction value with the actual measured value, analysis by the accuracy, obtain the trend of surface change around the subway station.

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