scholarly journals Prediction of Mine Dust Concentration Based on Grey Markov Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhou Xu ◽  
Guo Liwen ◽  
Zhang Jiuling ◽  
Qin Sijia ◽  
Zhu Yi
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Prediction Model ◽  
Spline Interpolation ◽  
Dust Concentration ◽  
Random Fluctuation ◽  
Chain Model ◽  
Data Sequence ◽  
Concentration Data ◽  
Mine Dust

Accurate quantitative analysis and prediction of dust concentration in mines play a vital role in avoiding pneumoconiosis to a certain extent, improving industrial production efficiency, and protecting the ecological environment. The research has far-reaching significance for the prediction of dust concentration in mines in the future. Aiming at the shortcomings of the grey GM (1, 1) model in forecasting the data sequence with large random fluctuation, a grey Markov chain forecasting model is established. Firstly, considering the timeliness of monitoring data, the new dust concentration data is supplemented by using the method of cubic spline interpolation in the original data sequence. Therefore, the GM (1, 1) model is established by the method of metabolism. Then, the GM (1, 1) model is optimized by the theory of the Markov chain model. According to the relative error range generated during the prediction, the state interval is divided. Subsequently, the corresponding state probability transition matrix is constructed to obtain the grey Markov prediction model. The model was applied to the prediction of mine dust concentration and compared with the prediction results of the BP neural network model, grey prediction model, and ARIMA (1, 2, 1) model. The results showed that the prediction accuracy of the grey Markov model was significantly improved compared with other traditional prediction models. Therefore, the rationality and accuracy of this model in the prediction of mine dust concentration were verified.

scholarly journals Landscape pattern and economic factors’ effect on prediction accuracy of cellular automata-Markov chain model on county scale

2020 ◽  
Vol 12 (1) ◽  
pp. 626-636
Author(s):  
Wang Song ◽  
Zhao Yunlin ◽  
Xu Zhenggang ◽  
Yang Guiyan ◽  
Huang Tian ◽  
...  
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Cellular Automata ◽  
Markov Model ◽  
Prediction Accuracy ◽  
Driving Mechanism ◽  
Economic Factors ◽  
Chain Model ◽  
The Impact ◽  
County Scale

AbstractUnderstanding and modeling of land use change is of great significance to environmental protection and land use planning. The cellular automata-Markov chain (CA-Markov) model is a powerful tool to predict the change of land use, and the prediction accuracy is limited by many factors. To explore the impact of land use and socio-economic factors on the prediction of CA-Markov model on county scale, this paper uses the CA-Markov model to simulate the land use of Anren County in 2016, based on the land use of 1996 and 2006. Then, the correlation between the land use, socio-economic data and the prediction accuracy was analyzed. The results show that Shannon’s evenness index and population density having an important impact on the accuracy of model predictions, negatively correlate with kappa coefficient. The research not only provides a reference for correct use of the model but also helps us to understand the driving mechanism of landscape changes.

Income disparities among hungarian micro-regions: The mover-stayer model

2008 ◽  
Vol 58 (2) ◽  
pp. 127-156 ◽  
Author(s):  
K. Major
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Markov Chain Model ◽  
Local Level ◽  
Relative Income ◽  
Chain Model ◽  
Ratio Test ◽  
Remarkable Feature ◽  
Homogeneity Assumption ◽  
Income Data

It is known that the simple Markov chain model overestimates the long run horizon mobility of the income distribution process. Dissolving the homogeneity assumption of the Markov model may lead to better forecasts. One generalisation of the Markov model, the Mover-Stayer model assumes heterogenous population: some units are moving according to a common Markov chain, but there are some (unknown) units that are not moving at all. They are called stayers.Based on the Frydman (1984) methodology if we compute both the Markov and Mover-Stayer models for Hungarian micro-regions income data, we find that the Mover-Stayer model fits better the regional relative income data than the simple Markov model. Using likelihood ratio test statistics we show that the difference is highly significant. The method is also applied for spatially conditioned data. The results show that the high persistence of relative income positions is a remarkable feature of the Hungarian economy in 1990–2003 both on a country-wide scale and local level. We also demonstrate that forecasts made on a less reliant model might lead to very ambiguous results.

The forecast of cultivate land quantity based on Grey-Markov model

2015 ◽  
Vol 5 (1) ◽  
pp. 127-136 ◽  
Author(s):  
Hongyan Huan ◽  
Qing-mei Tan
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Land Use Planning ◽  
Markov Chain Model ◽  
Arable Land ◽  
Jiangsu Province ◽  
Chain Model ◽  
Cultivated Land ◽  
Content Type ◽  
Investigation Methods

Purpose – The purpose of this paper is to employ the Grey-Markov Chain Model for the scale prediction of cultivated land and took an empirical research with the case of Jiangsu province. Design/methodology/approach – Along with China’s industrialization and urbanization accelerated, a large number of cultivated land converse into construction land. The change of utilization of cultivated land concerns national food security and sustainable development of economy and society. Due to the fact that the different investigation methods of arable land usually cause a uncertain. The Grey-Markov model combines the Grey GM(1,1) and Markov chain, with two advantages of dealing with poor information and long-term and volatile series. A numeric example of scale prediction of cultivated land in Jiangsu province is also computed in the third part of the paper. Findings – The results show that the Grey-Markov Chain Model has a higher prediction accuracy compared with GM (1,1), which is a reliable guarantee for the change of cultivated land resources. Practical implications – The forecast of cultivated land can provide useful information for the general land use planning. Originality/value – The paper confirmed the feasibility of the Grey-Markov model in scale prediction of cultivated land.

Aeroengine Gas Path Performance Parameter Prediction Based on GM(1,1) Rolling-Markov Chain Approach

2015 ◽  
Author(s):  
Ningbo Zhao ◽  
Jialong Yang ◽  
Shuying Li
Keyword(s):  
Markov Chain ◽  
Prediction Accuracy ◽  
Markov Chain Model ◽  
Performance Parameter ◽  
Random Fluctuation ◽  
Chain Model ◽  
Health State ◽  
Gas Temperature ◽  
Effective Estimate ◽  
Parameter Prediction

As one of the most important gas path performance parameters, the exhaust gas temperature (EGT) can provide more effective information about the health state of aeroengine. However, the changing process of aeroengine EGT is often affected by many uncertain factors and the sample data are relatively less, which make it difficult to predict its trend accurately by the traditional regression analysis method. Aiming at this problem, the GM(1,1) rolling-Markov chain model is proposed and used for aeroengine EGT prediction in this paper. Based on the equal dimensional new information theory, GM(1,1) rolling model is utilized to predict the changing trend of aeroengine EGT firstly. Then the Markov chain theory is used to solve the influence of random fluctuation on prediction accuracy, which can achieve an effective estimate of the non-linear parameter. As an example, the historical monitoring data of EGT from one aeroengine of Air China is used to verify the prediction performance of GM(1,1) rolling-Markov chain model. The analysis results show that this model has higher prediction accuracy and can effectively reflect the random fluctuation characteristics of EGT, which provides a new method for aeroengine gas path performance parameter prediction.

scholarly journals Impact the Temperature of Bangladesh: An Application of Markov Model

2019 ◽  
Vol 6 (2) ◽  
pp. 83-90
Author(s):  
Janardan Mahanta ◽  
Syed Tanjim Hossain ◽  
Imtiaz Reza
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Markov Chain Model ◽  
Chain Model ◽  
Stationary Probability ◽  
Order Markov Chain ◽  
Different Order

Markov chain model has been used to analyze the temperature of Bangladesh. Different order Markov chain model has constructed and their significance has been tested. Using Cramer’s , strength the association of temperature with the order of Markov chain has been measured. Stationary probability has been calculated, and there have been employed whether the temperature is stationary or not.

scholarly journals A New Multivariate Markov Chain Model for Adding a New Categorical Data Sequence

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Chao Wang ◽  
Ting-Zhu Huang ◽  
Wai-Ki Ching
Keyword(s):  
Markov Chain ◽  
Categorical Data ◽  
Numerical Experiments ◽  
Markov Chain Model ◽  
Chain Model ◽  
Data Sequence ◽  
Computational Resources

We propose a new multivariate Markov chain model for adding a new categorical data sequence. The number of the parameters in the new multivariate Markov chain model is only𝒪(3s) less than𝒪((s+1)2)the number of the parameters in the former multivariate Markov chain model. Numerical experiments demonstrate the benefits of the new multivariate Markov chain model on saving computational resources.

scholarly journals An Enhancement of Svm Based Semantically Enriched Variable Length Confidence Pruned Markov Chain Model Based Web Page Recommendation System

2020 ◽  
Vol 8 (5) ◽  
pp. 5039-5045
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Prediction Accuracy ◽  
Recommendation System ◽  
Markov Chain Model ◽  
Variable Length ◽  
Web Pages ◽  
Chain Model ◽  
Web Page ◽  
Semantic Variable

Semantic Variable Length Markov Chain Length Model (SVLMC) is a web page recommendation system which combined the fields of semantic web and web usage mining by the Markov transition probability matrix with rich semantic information extracted from web pages. Though it has high prediction accuracy, it has problem of high state space complexity. The high space complexity reduce the execution speed and reduce the performance of the system, which was resolved by Semantic Variable Length confidence pruned Markov Chain Model (SVLCPMC) model that provides high user satisfied recommendation and Confidence Pruned Markov Model (CPMM). The time consumption of CPMM was reduced by Support Vector Machine (SVM). But still the recommendation accuracy is still below the user satisfaction. So in this paper, quickest change detection using Kullback-Leibler Divergence method is introduced to improve the accuracy of recommendation generation by developing a scalable quickest change detection schemes that can be implemented recursively in a more complicated scenario of Markov model and it is included in the training data of SVM. Then the performance of web page recommendation is improved by ranking the web pages using page ranking technique. Thus the performance of web page recommendation generation system has been improved. The experiments are conducted to prove the effectiveness of the proposed work in terms of prediction accuracy, precision, recall, F1-measure, coverage and R measure.

SAFETY ASSESSMENT FOR SAFETY-CRITICAL SYSTEMS USING MARKOV CHAIN MODULAR APPROACH

2011 ◽  
Vol 18 (02) ◽  
pp. 139-157 ◽  
Author(s):  
YANGYANG YU ◽  
BARRY W. JOHNSON
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Safety Assessment ◽  
Markov Chain Model ◽  
Chain Model ◽  
Critical Systems ◽  
State Dependency ◽  
Failure State ◽  
Rate Functions ◽  
Homogenous Markov Chain

The Markov Chain Modular (MCM) approach is proposed in this paper in order to solve part of the failure-state dependency problem. The MCM approach completely avoids the failure-state dependency problem by avoiding the combinatorial modeling. To quantitatively assess safety, a new Markov chain modeling technique is developed to represent an m + 2 state homogenous Markov chain model using a three-state Markov model. The transition rate functions of the three-state Markov model can be determined by the transition rates of the m + 2 state Markov chain model. Given a series system has N modules and each module has O(m) operational states, the MCM approach reduces the operational states to O(N × m2) as opposed to O(m2N) by using the traditional Markov chain model.

Semantically Enriched Variable Length Markov Chain Model for Analysis of User Web Navigation Sessions

2014 ◽  
Vol 13 (04) ◽  
pp. 721-753 ◽  
Author(s):  
Suresh Shirgave ◽  
Prakash Kulkarni ◽  
José Borges
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Semantic Information ◽  
Markov Models ◽  
Markov Chain Model ◽  
Higher Order ◽  
Variable Length ◽  
Chain Model ◽  
Proposed Model ◽  
Better Than

The rapid growth of the World Wide Web has resulted in intricate Web sites, demanding enhanced user skills to find the required information and more sophisticated tools that are able to generate apt recommendations. Markov Chains have been widely used to generate next-page recommendations; however, accuracy of such models is limited. Herein, we propose the novel Semantic Variable Length Markov Chain Model (SVLMC) that combines the fields of Web Usage Mining and Semantic Web by enriching the Markov transition probability matrix with rich semantic information extracted from Web pages. We show that the method is able to enhance the prediction accuracy relatively to usage-based higher order Markov models and to semantic higher order Markov models based on ontology of concepts. In addition, the proposed model is able to handle the problem of ambiguous predictions. An extensive experimental evaluation was conducted on two real-world data sets and on one partially generated data set. The results show that the proposed model is able to achieve 15–20% better accuracy than the usage-based Markov model, 8–15% better than the semantic ontology Markov model and 7–12% better than semantic-pruned Selective Markov Model. In summary, the SVLMC is the first work proposing the integration of a rich set of detailed semantic information into higher order Web usage Markov models and experimental results reveal that the inclusion of detailed semantic data enhances the prediction ability of Markov models.

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