Hybrid Prediction Method of Aviation Unsafe Events Based on Grey Catastrophe and Regression Analysis

2013 ◽  
Vol 739 ◽  
pp. 759-763 ◽  
Author(s):  
Xu Sheng Gan ◽  
Jing Shun Duanmu ◽  
Jian Guo Gao
Keyword(s):  
Regression Analysis ◽  
Prediction Model ◽  
Prediction Method ◽  
Grey Prediction ◽  
Analysis Method ◽  
Analysis Model ◽  
Hybrid Prediction ◽  
Grey Prediction Model ◽  
Catastrophe Model ◽  
Events Data

In order to improve the prediction of aviation unsafe events, a hybrid prediction method of aviation unsafe events based on grey catastrophe and regression analysis method is proposed. At first, the catastrophe points (date) are found from original unsafe events data to establish the grey catastrophe model for predicting the future catastrophe points, and then the grey prediction model for these catastrophe points is built to calculate the catastrophe value corresponding to future catastrophe points. For non-catastrophe points, the appropriate regression analysis model needs to be established. Example validates its effectiveness.

Geodetic deformation forecasting based on multi-variable grey prediction model and regression model

2019 ◽  
Vol 9 (4) ◽  
pp. 464-471 ◽  
Author(s):  
Erkan Kose ◽  
Levent Tasci
Keyword(s):  
Regression Analysis ◽  
Prediction Model ◽  
Water Level ◽  
Influential Factor ◽  
Grey Prediction ◽  
Estimation Model ◽  
Content Type ◽  
Grey Prediction Model ◽  
Great Consistency ◽  
Deformation Level

Purpose The purpose of this paper is to examine the effectiveness of the multivariable grey prediction model in deformation forecasting. Design/methodology/approach Deformation in a dam can be seen because of many factors but without any doubt, the most influential factor is the water level. In this study, the deformation level of a point in the Keban Dam crest has been tried to be forecasted depending on the water level by the multivariable grey model GM(1,N). Regression analysis was used to test the accuracy of the prediction results obtained using the grey prediction model. Findings The results show that there is a great consistency between the grey prediction values and the actual values, and that the GM(1,N) produces more reliable results than the regression analysis. Based on the results, it can be concluded that the GM(1,N) is a very reliable estimation model for limited data conditions. Originality/value Different from the other studies in the literature, this study investigates deformation in a dam subject to the water level in the dam reservoir. The main contribution of the study to the literature is to suggest a relatively new procedure for estimating the deformation in the dams based on the water level.

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.

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.

Forecasting China's electricity consumption using a new grey prediction model

Energy ◽  
2018 ◽  
Vol 149 ◽  
pp. 314-328 ◽  
Author(s):  
Song Ding ◽  
Keith W. Hipel ◽  
Yao-guo Dang
Keyword(s):  
Prediction Model ◽  
Electricity Consumption ◽  
Grey Prediction ◽  
Grey Prediction Model
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