Bayesian discriminant analysis for prediction of coal and gas outbursts and applicatio

Wang, Chao; Wang, Enyuan; Xu, Jiankun; Liu, Xiaofei; Li, Ling

doi:10.1016/s1674-5264(09)60236-2

Cited by 7 publications

(5 citation statements)

References 1 publication

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“…The priori probability is used to describe awareness of the research objects before extracting samples and then the posteriori probability is obtained to modify the prior probability based on the extracted samples. [21,22] In the present study, a variety of statistical inferences were highlighted by the posteriori probability. A total of 443 samples in the training set were used to construct Bayesian discriminant criterion and discriminant functions of six tea categories.…”

Section: Distance Discriminant Analysis and Bayesian Discriminant Anamentioning

confidence: 71%

Discrimination of six tea categories coming from different origins depending on polyphenols, caffeine, and theanine combined with different discriminant analysis

Ning

Cao

et al. 2017

International Journal of Food Properties

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This study reported a quantitative method to discriminate six tea categories of 664 tea samples. The main components of tea including gallic acid (GA), caffeine, theanine, (−)-epigallocatechin gallate (EGCG), (−)-epigallocatechin (EGC), (−)-epicatechin gallate (ECG), (−)-epicatechin (EC), and (+)-catechin (C) were determined using high-performance liquid chromatography in accordance with the ISO detection standards. Genetic algorithm and stepwise discriminant method were used for factors selection based on nine indicators (GA, EGC, C, EC, EGCG, ECG, total catechins (TC), caffeine, and theanine). The results of factors selection were first optimized by combining improved indicators; subsequently, Bayesian discriminant and distance discriminant analyses were applied to discriminate tea categories. The results indicated that GA, EGC, caffeine, EGCG, EC, TC, theanine, EGC^1.25, and caffeine^2 combined with Bayesian discriminant analysis provide a feasible method of classifying six tea categories. The total identification rates were 94.13% in the training set and 92.31% in the prediction set. In addition, a satisfactory result was obtained for the discrimination of each tea category. ARTICLE HISTORY

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Section: Distance Discriminant Analysis and Bayesian Discriminant Anamentioning

confidence: 71%

Discrimination of six tea categories coming from different origins depending on polyphenols, caffeine, and theanine combined with different discriminant analysis

Ning

Cao

et al. 2017

International Journal of Food Properties

View full text Add to dashboard Cite

show abstract

“…(2008) ascertained the relationship between the factors and hazards of coal and gas outbursts by multifactor pattern recognition and then predicted the hazard of each prediction unit. Wang et al. (2010) established a predictive model based on Bayesian discriminant analysis and attained reasonable results that completely coincided with the actual situation.…”

Section: Introductionmentioning

confidence: 75%

“…Zhang et al (2008) ascertained the relationship between the factors and hazards of coal and gas outbursts by multifactor pattern recognition and then predicted the hazard of each prediction unit. Wang et al (2010) established a predictive model based on Bayesian discriminant analysis and attained reasonable results that completely coincided with the actual situation. Zhang and Lowndes (2010) applied a coupled artificial neural network and fault tree analysis model to the prediction of coal and gas outbursts, and the results indicated that the combined solution method could explicitly recognize model relationships between the geological conditions and the potential risk of outbursts.…”

Section: Introductionmentioning

confidence: 75%

Prediction of coal and gas outbursts by a novel model based on multisource information fusion

Yang

Jiang

2020

Energy Exploration & Exploitation

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As one type of geologic disaster, coal and gas outbursts seriously threaten safe production in coal mines, restricting the sustainable development of the mining industry. However, coal and gas outbursts are difficult to forecast due to their uncertainty and the limitation of sample size, which affect the accuracy of the traditional prediction methods to some extent. Therefore, this study developed a novel model based on multisource information fusion to realize the predictive progress of coal and gas outburst disasters. Through the application of Dempster-Shafer theory, a method of multisource information fusion, the proposed model combined the results of different forecasting approaches, including conventional techniques and an emerging method based on artificial intelligence. To enhance the performance of the established model, this study improved Dempster-Shafer theory and verified its effectiveness in dealing with highly conflicting information. We then applied this model to the No. 3 coal seam of the Xinjing coal mine, Shanxi Province, China. The fused prediction accurately reflected the situation of outburst hazards and showed good compensation for false prediction. An analysis of the results concluded that the model based on multisource information fusion increases the credibility of the forecast, which might provide technical support for safe coal mine production.

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“…The previous single disaster prediction method is no longer applicable, and the prediction difficulty of coal-gas compound dynamic disaster is greatly increased [12]. At present, the previous research on the prediction of rockburst and coal-gas outburst is quite fruitful [13][14][15][16][17][18]. However, the research on the prediction of coal-gas compound dynamic disaster is relatively less.…”

Section: Introductionmentioning

confidence: 99%

Study on Prediction of Coal-Gas Compound Dynamic Disaster Based on GRA-PCA-BP Model

Wang

2021

Geofluids

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The intensity and depth of China’s coal mining are increasing, and the risk of coal-gas compound dynamic disaster is prominent, which seriously restricts the green, safe, and efficient mining of China’s coal resources. How to accurately predict the risk of disasters is an important basis for disaster prevention and control. In this paper, the Pingdingshan No. 8 coal mine is taken as the research object, and the grey relational analysis (GRA), principal component analysis (PCA), and BP neural network are combined to predict the coal-gas compound dynamic disaster. First, the weights of 13 influencing factors are sorted and screened by grey relational analysis. Next, principal component analysis is carried out on the influencing factors with high weight value to extract common factors. Then, the common factor is used as the input parameter of BP neural network to train the previous data. Finally, the coal-gas compound dynamic disaster prediction model based on GRA-PCA-BP neural network is established. After verification, the model can effectively predict the occurrence of coal-gas compound dynamic disaster. The prediction results are consistent with the actual situation of the coal mine with high accuracy and practicality. This work is of great significance to ensure the safe and efficient production of deep mines.

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Bayesian discriminant analysis for prediction of coal and gas outbursts and applicatio

Cited by 7 publications

References 1 publication

Discrimination of six tea categories coming from different origins depending on polyphenols, caffeine, and theanine combined with different discriminant analysis

Discrimination of six tea categories coming from different origins depending on polyphenols, caffeine, and theanine combined with different discriminant analysis

Prediction of coal and gas outbursts by a novel model based on multisource information fusion

Study on Prediction of Coal-Gas Compound Dynamic Disaster Based on GRA-PCA-BP Model

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