scholarly journals A Novel Multivariable MGM (1, m) Direct Prediction Model and Its Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yuanping Ding ◽  
Ye Li
Keyword(s):  
Prediction Model ◽  
Development Trend ◽  
Time Development ◽  
Mutual Transformation ◽  
Vehicle Ownership ◽  
Foundation Pit ◽  
Comparison Results ◽  
Verhulst Model ◽  
Polynomial Term ◽  
System Variables

With regard to the traditional MGM (1, m) model having jumping error in solving process, an MGM (1, m) direct prediction model (denoted as DMGM (1, m) model) is proposed and its solution method is put forward at first. Second, considering the inherent time development trend of system behavior sequence is ignored in the DMGM (1, m) model, the DMGM (1, m) model is optimized by introducing a time polynomial term, and the optimized model can be abbreviated as TPDMGM (1, m, φ ) model. Subsequently, it is theoretically proved that the TPDMGM (1, m, φ ) model can achieve mutual transformation with the traditional MGM (1, m) model and the DMGM (1, m) model by adjusting the parameter values. Finally, two case studies about predicting the deformation of foundation pit and Henan’s vehicle ownership have been carried out to validate the effectiveness of proposed models. Meanwhile, the MGM (1, m) model and Verhulst model are established for comparison. Results show that the modeling performance of four models from superior to inferior is ranked as TPDMGM (1, m, φ ) model, DMGM (1, m) model, MGM (1, m) model, and Verhulst model, which on the one hand testifies the correctness of defect analysis of the MGM (1, m) model and on the other hand verifies that the TPDMGM (1, m, φ ) model has advantages in predicting the system variables with mutual relation, mutual restriction, and time development trend characteristic.

scholarly journals Fire Risk Assessment Models Using Statistical Machine Learning and Optimized Risk Indexing

2020 ◽  
Vol 10 (12) ◽  
pp. 4199
Author(s):  
Myoung-Young Choi ◽  
Sunghae Jun
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Logistic Regression ◽  
Prediction Model ◽  
Fire Risk ◽  
Fire Occurrence ◽  
Statistical Machine Learning ◽  
Fire Insurance ◽  
Comparison Results ◽  
Fire Risk Assessment

It is very difficult for us to accurately predict occurrence of a fire. But, this is very important to protect human life and property. So, we study fire hazard prediction and evaluation methods to cope with fire risks. In this paper, we propose three models based on statistical machine learning and optimized risk indexing for fire risk assessment. We build logistic regression, deep neural networks (DNN) and fire risk indexing models, and verify performances between proposed and traditional models using real investigated data related to fire occurrence in Korea. In general, fire prediction models currently in use do not provide satisfactory levels of accuracy. The reason for this result is that the factors affecting fire occurrence are very diverse and frequency of fire occurrence is very sparse. To improve accuracy of fire occurrence, we first build logistic regression and DNN models. In addition, we construct a fire risk indexing model for a more improved model of fire prediction. To illustrate comparison results between our research models and current fire prediction model, we use real fire data investigated in Korea between 2011 to 2017. From the experimental results of this paper, we can confirm that accuracy of prediction by the proposed method is superior to the existing fire occurrence prediction model. Therefore, we expect the proposed model to contribute to evaluating the possibility of fire risk in buildings and factories in the field of fire insurance and to calculate the fire insurance premium.

Measurement of shock effect following change of one-child policy based on grey forecasting approach

Kybernetes ◽  
2018 ◽  
Vol 47 (3) ◽  
pp. 559-586 ◽  
Author(s):  
Naiming Xie ◽  
Ruizhi Wang ◽  
Nanlei Chen
Keyword(s):  
Sex Ratio ◽  
Total Population ◽  
Development Trend ◽  
General Development ◽  
Content Type ◽  
One Child Policy ◽  
Child Policy ◽  
Grey Verhulst Model ◽  
Verhulst Model ◽  
The One

Purpose This paper aims to analyze general development trend of China’s population and to forecast China’s total population under the change of China’s family planning policy so as to measure shock disturbance effects on China’s population development. Design/methodology/approach China has been the most populous country for hundreds of years. And this state will be sustained in the forthcoming decade. Obviously, China is confronted with greater pressure on controlling total scale of population than any other country. Meanwhile, controlling population will be beneficial for not only China but also the whole world. This paper first analyzes general development trend of China’s population total amount, sex ratio and aging ratio. The mechanism for measurement of the impact effect of a policy shock disturbance is proposed. Linear regression model, exponential curve model and grey Verhulst model are adopted to test accuracy of simulation of China’s total population. Then considering the policy shock disturbance on population, discrete grey model, DGM (1, 1), and grey Verhulst model were adopted to measure how China’s one-child policy affected its total population between 1978 and 2015. And similarly, the grey Verhulst model and scenario analysis of economic developing level were further used to forecast the effect of adjustment from China’s one-child policy to two-child policy. Findings Results show that China has made an outstanding contribution toward controlling population; it was estimated that China prevented nearly 470 million births since the late 1970s to 2015. However, according to the forecast, with the adjustment of the one-child policy, the birth rate will be a little higher, China’s total population was estimated to reach 1,485.59 million in 2025. Although the scale of population will keep increasing, but it is tolerable for China and sex ratio and trend of aging will be relieved obviously. Practical implications The approach constructed in the paper can be used to measure the effect of population change under the policy shock disturbance. It can be used for other policy effect measurement problems under shock events’ disturbance. Originality/value The paper succeeded in studying the mechanism for the measurement of the post-impact effect of a policy and the effect of changes in China’s population following the revision of the one-child policy. The mechanism is useful for solving system forecasting problems and can contribute toward improving the grey decision-making models.

scholarly journals A Prediction Model of Pressure Loss of Cement Slurry in Deep-Water HTHP Directional Wells

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 8180
Author(s):  
Kunhong Lv ◽  
Hao Huang ◽  
Xingqiang Zhong ◽  
Yian Tong ◽  
Xingjie Ling ◽  
...  
Keyword(s):  
Prediction Model ◽  
Deep Water ◽  
Pressure Loss ◽  
Maximum Error ◽  
Stability And Convergence ◽  
Transient Temperature ◽  
Cement Slurry ◽  
Hydration Kinetics ◽  
Comparison Results ◽  
Temperature And Pressure

The exploitations of deep-water wells often use directional well drilling to reach the target layer. Affected by special environments in deep water, the prediction of pressure loss of cement slurry is particularly important. This paper presents a prediction model of pressure loss suitable for deep-water directional wells. This model takes the complex interaction between the temperature, pressure and hydration kinetics of cement slurry into account. Based on the initial and boundary conditions, the finite difference method is used to discretize and calculate the model to ensure the stability and convergence of the result calculated by this model. Finally, the calculation equation of the model is used to predict the transient temperature and pressure loss of Wells X1 and X2, and a comparison is made between the predicted value and the monitoring data. The comparison results show that the maximum error between the temperature and pressure predicted by the model and the field measured value is within 6%. Thus, this model is of high accuracy and can meet the needs of site construction. It is concluded that this result can provide reliable theoretical guidance for temperature and pressure prediction, as well as the anti-channeling design of HTHP directional wells.

Study on Displacement Prediction Model of Foundation Pit Named as GA-transFcn Model

2013 ◽  
Vol 5 (5) ◽  
pp. 1250-1258
Author(s):  
Qiang Song ◽  
Junjian Zhang ◽  
Wei Zhang ◽  
Yunsheng Liu ◽  
Xianli Cui
Keyword(s):  
Prediction Model ◽  
Foundation Pit ◽  
Displacement Prediction

A novel adaptive discrete grey prediction model for forecasting development in energy consumption structure—from the perspective of compositional data

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wuyong Qian ◽  
Hao Zhang ◽  
Aodi Sui ◽  
Yuhong Wang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Compositional Data ◽  
Development Trend ◽  
Grey Prediction ◽  
Content Type ◽  
Consumption Structure ◽  
Grey Prediction Model ◽  
Proposed Model

PurposeThe purpose of this study is to make a prediction of China's energy consumption structure from the perspective of compositional data and construct a novel grey model for forecasting compositional data.Design/methodology/approachDue to the existing grey prediction model based on compositional data cannot effectively excavate the evolution law of correlation dimension sequence of compositional data. Thus, the adaptive discrete grey prediction model with innovation term based on compositional data is proposed to forecast the integral structure of China's energy consumption. The prediction results from the new model are then compared with three existing approaches and the comparison results indicate that the proposed model generally outperforms existing methods. A further prediction of China's energy consumption structure is conducted into a future horizon from 2021 to 2035 by using the model.FindingsChina's energy structure will change significantly in the medium and long term and China's energy consumption structure can reach the long-term goal. Besides, the proposed model can better mine and predict the development trend of single time series after the transformation of compositional data.Originality/valueThe paper considers the dynamic change of grey action quantity, the characteristics of compositional data and the impact of new information about the system itself on the current system development trend and proposes a novel adaptive discrete grey prediction model with innovation term based on compositional data, which fills the gap in previous studies.

Study and Prediction on the Development Trend of Chinese Sports Venues on the Basis of Grey Prediction Model

2014 ◽  
Vol 687-691 ◽  
pp. 1588-1591
Author(s):  
Yu Ming Li
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Development Trend ◽  
Survey Method ◽  
Grey Theory ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Sports Events ◽  
Grey Number ◽  
Sports Venues

The grey forecasting method in most of the existing prediction, there are little research on the problem of interval prediction. This paper presents the concept of synthesis of grey number and grey theory, describes the properties of the synthetic ash gray because the grey prediction model is proposed the importance to build the stadium for the first time. On this basis, the thesis analyze the research background and significance, then describes some scene at home and abroad, and then analyzes the important characteristics of large-scale sports events and sports stadium construction, at last, the paper does some prediction about the construction of the stadium. This paper mainly uses the literature investigation method combined with a survey method.

Deformation Prediction of Slopes Based on Grey Verhulst Model

2012 ◽  
Vol 594-597 ◽  
pp. 347-351
Author(s):  
Zhou Qiang
Keyword(s):  
Rock Mass ◽  
Prediction Model ◽  
Rock Slope ◽  
Dynamic Prediction ◽  
Deformation Prediction ◽  
Grey Verhulst Model ◽  
Rock Mass Deformation ◽  
Verhulst Model ◽  
Slope Rock ◽  
Mass Deformation

The grey Verhulst nonlinear differential dynamic prediction model is applied to the prediction of the development of rock slope deformation in this paper. And experimental results show that grey Verhulst model is feasible to predict the slope rock mass deformation.

Prediction model of network security situation based on genetic algorithm and support vector machine

2021 ◽  
pp. 1-9
Author(s):  
Jingyu Xing ◽  
Zheng Zhang
Keyword(s):  
Simulated Annealing ◽  
Network Security ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Development Trend ◽  
Training Sample ◽  
Learning Ability ◽  
Support Vector ◽  
Machine Model ◽  
Correlation Vector

In order to predict the development trend of network security situation more accurately, this paper proposes an improved vector machine model by simulated annealing optimization to improve network security situation prediction. In the process of prediction, the sample data of phase space reconstruction network security status is first formed to form training sample set, and then the simulated annealing method is improved. The correlation vector machine is the optimization of correlation vector machine with simulated degradation algorithm embedded in the calculation process of objective function. The network security situation prediction model is obtained through super parameters to improve the learning ability and prediction accuracy. The simulation results show that this method has higher prediction accuracy better than the correlation vector machine model optimized by Elman and simulated annealing. This method can describe the change of network security well.

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