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An empirical study of early warning model on the number of coal mine accidents in China
Safety Science 123 (2020) 104559
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Safety Science journal homepage: www.elsevier.com/locate/safety
An empirical study of early warning model on the number of coal mine accidents in China
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Qing Liu, Jian Liu , Jinxin Gao, Jingjing Wang, Jing Han School of Civil and Environmental Engineering, University of Science and Technology Beijing, No. 30 Xueyuan Road, Haidian District, Beijing 100083, PR China
A R T I C LE I N FO
A B S T R A C T
Keywords: Coal mine accidents BP model VAR model Early warning
Coal mining is an important energy industry as well as high risk industry with high accident rate. The objective of this study is to establish an early warning model for coal mine accidents. Based on the accident data in the recent 11 years, it is concluded that the coal mine accidents data has the characteristics of large fluctuations, small sample size, small value and large numerical nonlinear characteristics. After literature consulting, data analysis and variable comparison, Policy intervention degree (PID), Manufacturing Purchasing Managers' Index (PMI), Producer Price Index for Industrial Products (PPI), Main raw material purchase price index (RMPPI), Employment index (EI) are selected as the auxiliary variables to construct the early warning model. VAR model is applied to determine the model structure. Results show that BP neural network model is suitable for the prediction of the number of coal mine accident.
1. Introduction Coal mining is the most important energy industry as well as one of the highest riskindustries in China. In 2016, approximately 77% of energy production was generated by coal in China (Tang and Peng, 2017). Meanwhile, there were 249 coal mine accidents in coal industry resulting in 526 deaths (Guo et al., 2015). There is an inherent risk in coal mine, and because of the huge amount of employee involved in and the serous accident results, having a great influence on society. With the development of economic and the progress of coal mining technology, coal mechanization degree and efficiency of raw coal are unceasingly increasing, and the mine production capacity is constant enhancing, but the development is unbalanced and inadequate. Complex mining conditions of coal mine and large demand of personnel cause the working conditions in coal mine varied and dangerous, bringing a great challenge for safety management. Thus it attracts more and more attention on safety production strengthening and accident prevention. Traditional accident treatments are no longer able to satisfy the expectation and need on safety management, and pre-accident treatments are getting more and more important. Accident earlywarning model plays a more important role in safety management for its help to pre-accident treatments. The foundation of accident early warning is the causality and statistical inevitability of accidents, which can be seen as the comprehensive results of the recurrence of time trend and other disturbance
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factors. As a result, making full use of the internal mechanism of the accident and the accumulate relevant information is the key to get reliable early warning results. Many existing works dealing with accident prediction are devoted to taking qualitative approaches in order to understand the potential reasons causing coal mine accidents, such as surveys (Li et al., 2017), interviews (Li et al., 2016), or simulations (Qi et al., 2014), rather than collecting real field data. In recent years, statistical methods have been applied to describe the trend of accident. Tang et al (2016) deduced a theoretical line prediction formula for calculating the amount of gas released from boreholes during drilling, with the parameters of ground stress, gas pressure, and coal strength around the borehole during drilling, which could accurately reflect the outburst hazard in front of working faces. Janusz et al. (2017) compared the prediction ability between expert methods which serve as a standard in the coal mining industry and state-of-the-art machine learning methods with high-dimensional time series data, and they both have their own advantages. Wei et al. (2011) found new information models had a better prediction effect over grey prediction models and metabolism models to realize the dynamics of coal mine gas emission projections, which was verified and analyzed by the example of the mine in Hegang. But there are few studies on early warning of the total amount of coal mine accidents. At present, some safety production information collection and processing systems have been equipped with the function of preliminary accident number estimates with accident related index,
Corresponding author. E-mail address: [email protected] (J. Liu).
https://doi.org/10.1016/j.ssci.2019.104559 Received 15 October 2018; Received in revised form 28 October 2019; Accepted 24 November 2019 0925-7535/ © 2019 Published by Elsevier Ltd.
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but the precision of the early warning models could be further improved. This paper proposes a combination model to realize the early warning of the number of coal mine accidents. For quantitative forecasting, it is necessary to infer a forecasting model based on historical data. Linear models have outstanding advantages among forecasting models. On one hand, linear models are easy to understand the correlation analysis between factors and combine the existing qualitative experience. On the other hand, based on strict statistical analysis framework, they can better verify the theoretical assumptions. The disadvantage of linear models is that it is difficult for them to express complex relationships, and the prediction accuracy is relatively low. Nonlinear models based on deterministic forms, on the contrary, can express complex correlations. But due to the limitation of the amount of real data, it is often difficult for nonlinear models to obtain an ideal model form. Nonlinear models based on machine learning have prominent advantages in the expression of complex correlation and the determination of model form, while the analysis of the relationship between factors is not mature enough. Therefore, a combination model is established by combining linear models with BP neural network model.
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The number of accidents is a very important accident statistical index, which can represent the occurrence trend of the accident, while the number of deaths in each accident still has a certain randomness. Therefore, comparing to the number of accident death toll, once the severity level of the accident is determined, the randomness of the time series of coal mine accident number is relatively smaller, and as a result it can represent the changes of safety production situation in a certain area in a more comprehensive way. The major accident data in this paper was collected from the web of State Administration of Work Safety of China and the web National Bureau of Statistics of the People’s Republic of China. Major accidents here are accidents caused 3 people or more died, or 10 people or more seriously injured, or more than 50 million yuan direct economic losses. Fig. 1 shows the major accident time series of coal mine accident. The number of major accidents is more in amount than the number of severe accidents, which has a more obvious statistical law over the later. Due to the complete records of major accidents, there are less problems of data missing and distorted. Therefore, the reliability and authenticity of the accident data are higher than that of the total accidents. As a result, the number of coal mine major accidents is chosen as the study subject in this paper. At present, limited by historical records of accident in the coal mine
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Heinrich accident causal chain theory shows that the occurrence of accidents is caused by a series of events that have a causal relationship. Coal mines have varied mining conditions, a wide area distribution, different processes, leading to the different status, which means it is hard to fully grasp the direct and indirect causes of coal mine accidents. But long-term practice and research has allowed us to get a lot of information about the factors that are related to the accident of coal mine, which makes it possible to search for the important cause of it. Many scholars (Huang et al, 2005, Zhao and Tao, 2012, Zhang, 2011, He and Nie, 2008, Wang, 2008, Zhao and He, 2013, Kong and Tian, 2007, Ren et al, 2008, Liu, 2010) have done long-term research on the macro factors affected the coal mine accident, which are mainly including four aspects: the indexes of economic development, the indexes of social structure, the indexes of population quality and the indexes of policy intervention. The indexes of social structure and the indexes of population quality data statistic once a year or once a quarter, data’s monthly changes couldn’t be obtained. What’s more, the range of data changes from month to month is very small which can be neglected. Therefore, only fixed asset investment growth rate is considered as a factor, presented
The number of coal mine accident
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2.2. Auxiliary indexes selected
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industry in China, the coal mine accidents data from 2006 to 2016 are suitable as the study subject for its consistent statistical standard and good continuity. To improve the accuracy and the sensitivity of the early warning, and enhance the practicability of the early warning results, monthly data is selected as the basis in this paper. As shown in Fig. 2, accident time series has the properties of downward trend, relatively large fluctuations, a sharp graphics, relatively small numerical value, data fluctuation range from 9 to 189. There isn’t obvious periodicity in the series, and the fluctuation range of which is gradually decreasing. In most months, the number of major coal mine accidents is less than 150. There are only 2 months in which the number of major coal mine accidents is larger than 180. In most months, the number of coal mine accidents is no more than 70. The mean value of the number of major coal mine accidents is 81.04, and the standard deviation of it is 49.12. The coefficient of variation is the ratio of the standard deviation and the mean. The coefficient of variation of the number of major coal mine accidents is 0.6061, which indicates that time series has a feature of big dispersion. Skewness value is 0.3667 > 0, indicating that the data has a positive deviation, also known as right skewness, which shows that the accident number distribution is concentrated in a small numerical region. The kurtosis value is 1.9615 < 3, indicating a lack of kurtosis, shows there are extreme values. The overall characteristics of the number of accidents data is: with a downward trend, small samples, large fluctuations.
2.1. The number of coal mine accident
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Fig. 2. Histogram of the number of coal mine accident.
2. Study subject
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Fig. 1. The number of coal mine accident. 2
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as Completed in Fixed Assets, Accumulated, and the rest are not contained as auxiliary indexes for predicting model. At the same time, in order to reflect the social structure changes, use Employee Index (EI) as the social structure indicator. The influence factors directly related to coal mine accidents such as coal seam occurrence conditions, ignition tendency of coal mine, production method of coal mine, mining velocity and so on, are not considered. Because they vary greatly from region to region, and it is difficult to quantify the unified index as well as to obtain relevant data, coupled with the fact that influence of the macroscopic number of accidents can’ t be estimated. Considering the number of coal mine accidents is related to safety investment, and enterprise safety input is closely related to the profit of its production, the Main raw material purchase price index (RMPPI), Producer Price Index for Industrial Products (PPI) are chosen as auxiliary indexes. Because the purpose of paper is to predict monthly coal mine accident number, we mainly consider indexes which are in macro level, monthly data available and having access to complete data from 2006 to 2015. In order to meet these demands, there is a need to screening and replace the index of predicting according to the objective substitution. Table 1 shows the selected indicators. Factors screened out are as follows: RMPPI, PMI, PPI, EI, PID.
Table 2 Results of unit root test.
(2)
−1.154673 −8.985810 −3.244707 −3.494752 −2.913454 −3.961365 −6.967925
0.6922 0.0000 0.0197 0.0096 0.0465 0.0022 0.0000
VAR model is a kind of econometric model, which describes the linear relationship between multiple time series. It extends the single variable autoregressive model (AR model) through allowing multiple endogenous variables. All variables in VAR enter the model in the same way: each variable has an equation, explain its evolution with its own lag value, the lag value of other model variables and the error term. In recent years, VAR model has been widely used to explore the interaction among economic variables on macroeconomic issues in econometric analysis. For VAR model, the mathematical expression is shown below.
yt = B0 + B1 yt − 1 + B2 yt − 2 + ⋯+Bp yt − p + ut
(3)
where t represents the time step; z t − 1is the value of 1 lag of the test data; Δz t is the difference between test data in time step t and time step t-1; μ represents the displacement term; α andρ are the coefficient of t and Table 1 The Auxiliary index of coal mine accident prediction. Category
Initial index
Selected index
Indexes of economic development
Gross Domestic Product (GDP), per capita gross national product (GNP)
Indexes of social structure
Agricultural output value accounted for the proportion of GDP, the Secondary industrial output value accounted for the proportion of GDP, the Tertiary industrial output value accounted for the proportion of GDP, Research investment accounted for the proportion of GDP, Fixed asset investment growth rate, Exports of goods and services account for the proportion of GDP, education funds accounted for the proportion of GDP Enrollment rate of middle school students, Natural Growth Rate, infant Death rate policy intervention degree
Main raw material purchase price index (RMPPI), Manufacturing Purchasing Managers' Index (PMI), Producer Price Index for Industrial Products (PPI) Employment index (EI)
Indexes of population quality indexes of policy intervention
(4)
where yt represents the value of all the variables in time step t; yt − p represents the value of p lag of the variables; Bp represents the coefficient of p lag of the yt − p ; t represents the time step. ut has a normal distribution with the mean value of 0 and variance of δ 2 . Due to the presence of contemporaneous correlation, any change of one explanatory variable will likely trigger the changes of other explanatory variables and thus jointly influence the response variables. Quantifying the contribution of each individual explanatory variable to the response variable is very important to build the structure of prediction model. The impulse response function is able to help to study the dynamic and convoluted relationship between variables in a clearer manner. When the disturbance term of an explanatory variable plus one-unit of standard deviation, other disturbance terms of explanatory variables still remain constant, and the corresponding value of the explanatory variable is called impulse response function. Impulse response explains the contemporaneous correlation and lagged dynamic influence
where x is the raw data; X is the normalized data; x max and x min are the maximum and minimum value in the raw data. Before building VAR model, it is important to confirm the stationarity of data sets and the existence of long-run correlation among the input data. When data is non-stationary, it might cause spurious regressions, leading to untreatable results. The formulas of the stationarity are as follows.
Δz t = μ + αt + ρz t − 1 + ut , z 0 = 0, ut ~N (0, δ 2)
ACCI PID PMI EI PPI RMPPI DACCI
3.2. VAR model
In order to eliminate the order of magnitude difference between inputs and output, the normalization method was used to preprocess each input and output before network learning. The general formulas of normalization and anti-normalization are described below.
x = x min + X × (x max − x min )
Prob.
z t − 1. If ρ = 0, it means that the data set y is unstable. If ρ = 1, then it means that the data set y is stable. Differential processing is performed on the unstable sequence before normalized again. The results are in Table 2.
3.1. Data pre-processing
(1)
t-Statistic
Except variable ACCI, all other variables passed the unit root test.
3. Model development
x − x min X= x max − x min
Variable
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— policy intervention degree (PID)
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Table 3 Tested parameter settings of BP model.
between variables, enable us to get an estimate about how long and how much would one variable affect another. 3.3. BP network BP (Back Propagation) neural network has the ability of selflearning, self-organizing, self-adaptive and powerful nonlinear mapping. So it is often applied to the study of uncertain problem involving complex causality. BP neural network is one of the most widely used neural network models at present. Itcan learn and store a large number of input-output mapping relations without prior revealing the mathematical equations describing the mapping relationship. A three-layer BP neural network was developed in this study for its sufficient ability to approximate any continuous nonlinear functions. Its architecture is shown in Fig. 3. BP neural network consists of the input layer, hidden layer and output layer. The rule of learning is using the gradient descent method, to adjust the weights and thresholds of the network through reverse propagation, and making the sum of the square sum of the error between the actual output of the neural network and the desired output minimum. The learning process of the neural network includes two stages: forward propagation and backward propagation. In the forward propagation process, the transmission information is processed from the hidden layer by layer to the output layer, and the state of each layer of neurons only affects the state of the next layer of neurons. In the backward propagation process, the difference between the ideal output and the actual output is reversed by the connection path, and the weights of the neurons in each layer are adjusted by the gradient descent method.
i=1
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The established BP model has an input of 24 variables, the fitting results are shown in Fig. 6. The number of hidden layer neurons was determined by empirical formula as below, and 10 neurons of Hidden layer were selected. h represents the number of hidden layer neurons, m represents the number of input layer neurons, n represents the number of output layer neurons, a represents a constant of 0–10.
h=
m+n +a
(7)
The value R was applied to measure the goodness of fit. The R value of the training set is 0.97585, while that of validation set and testing set are 0.89832 and 0.93708 respectively, which suggests that the BP model has a good fitness of the data set to predict. In the prediction results, it can be seen that the training goodness of fit is obviously higher than the test and verification goodness of fit, which is in line with the general prediction situation, indicating that the selected model was still not the optimal model. According to the ideal situation, the number of the three should be completely equal, which indicates that the prediction model is more or less over-fitting. However, it can be seen that the goodness of fit has reached 0.90, the representative model has achieved good results, and the goodness of fit has also reached 0.94 as expected, indicating that the model is worthy
(5)
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4.2. BP prediction
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The number of neurons in hidden layer Learning rate The number of iterations
The VAR models with different lag orders are estimated based on five criteria of LogL, LR, FPE, AIC, SC and HQ, and the estimated results of are shown in Table 4. When the lag order is 2 or 11, there are two principles with optimal result. The VAR model was established by selecting the lag order of 2 by the simplest principle of the model. The result of inverse roots of AR characteristic polynomial for the established VAR model is shown in Fig. 4. The absolute value of the characteristic root of the characteristic equation corresponding to the VAR model falls within the unit circle, which means the value of it is less than 1, showing the VAR model is stationary. The impulse response of the difference of the number of major coal mine accidents in China is shown in Fig. 5. The variable DACCI refers to the difference of the number of major coal mine accidents after preprocessing. The other variables refer to preprocessing results of variables corresponds to its name. When the disturbance term of the variable PID, PMI, EI, PPI, RMPPI plus one-unit of standard deviation, other disturbance terms of explanatory variables still remain constant, the variable DACCI is affected in 1–4 times step lag. As a result, 4 times step lag was selected as the structure of input of BP model.
∑ (x i ̂ − x i )2 R=
L n ep
4.1. Model structure determination
VAR model in this study was developed with all the variables after data pre-processing. The suitable VAR lag order was selected to establish a suitable VAR model based on the optimal results of 5 criteria such as AIC and SC. The impulse response analysis of the VAR model was made to determine the lag order of each auxiliary variable and the BP model was established based on it. BP model in this study was developed using 125 sets of experimental data. During the learning process, 88 data sets were selected as the training set and 19 data sets were selected as the validation set. During the testing process, the rest 18 data sets were used as the test set. Furthermore, the model performance was evaluated by the correlation coefficient(R) and root mean squared error (RMSE) which could be calculated as N
Levels
4. Results and discussion
3.4. Model building
∑
Definition
where N is the number of all samples; x is the experimental target output; x ̂ is the neural network prediction output after the anti-normalization process. x¯ is the mean value of target output. The hidden neuron number and population size are important parameters influencing the model performance, but no general formula has been found to determine these two parameters currently. Therefore, in order to determine appropriate model parameters, this study experimented with various parameter settings (see Table 3), using MATLAB R2016b.
Fig. 3. The architecture of three-layer BP neural network.
RMSE =
Parameter
(6) 4
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Table 4 VAR lag order selection criteria. Lag
LogL
LR
FPE
AIC
SC
HQ
0 1 2 3 4 5 6 7 8 9 10 11 12
362.4853 770.9978 901.1018 932.2710 964.2144 1001.455 1034.659 1076.169 1115.959 1161.646 1208.242 1334.620 1385.852
NA 768.9647 231.7821 52.38509 50.46526 55.07798 45.76058 53.02082 46.81163 49.14254 45.42147 110.4478* 39.60831
1.01e−10 1.93e−13 3.98e−14 4.36e−14 4.77e−14 4.84e−14 5.36e−14 5.28e−14 5.51e−14 5.40e−14 5.46e−14 1.53e−14* 1.63e−14
−5.991349 −12.25206 −13.83364 −13.75245 −13.68428 −13.70512 −13.65813 −13.75073 −13.81443 −13.97724 −14.15533 −15.67428 −15.93029*
−5.851225 −11.27120 −12.01203* −11.09010 −10.18118 −9.361280 −8.473550 −7.725407 −6.948360 −6.270427 −5.607770 −6.285981 −5.701246
−5.934449 −11.85376 −13.09395* −12.67136 −12.26178 −11.94122 −11.55284 −11.30404 −11.02634 −10.84775 −10.68443 −11.86199 −11.77660
Inverse Roots of AR Characteristic Polynomial
of affirmation in terms of prediction ability. The prediction result is shown in Fig. 7. The prediction results have accurately reflected the actual fluctuations, which suggests that the BP model have a good early warning ability. The RMSE of the established BP model is 7.80, which is far less than the average number of relative monthly data.
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4.3. Discussion
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In this paper, the VAR model is established to determine the lag interval of the input variables, based on which the structure of the input was determined. The number of major coal mine accidents in the next month is predicted by the BP neural network, and the early warning function of the macroscopic coal mine accidents is realized. Macroeconomic, social and policy variables are considered in the established model, thus representing their impact on coal mine accidents to a certain extent. The results show that the model has a good fitting effect. This largely attributes to the nonlinear relation between the relative variables of macro economy, society and policy and the number of major coal mine accidents, and the BP neural network’s advantages in dealing with the nonlinear relation. In the research of macro prediction and macro early warning, due to the factors influencing the accidents in a complex way, the choice of lag time for these data is a very important foundation for model establishment. This article applied VAR model to select the lag period of
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Fig. 4. The result of inverse roots of AR characteristic polynomial.
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Fig. 5. The impulse response of the difference of the number of major coal mine accident in China. 5
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Fig. 6. The fitting result of BP neural network.
to apply the model which depends more on experience to determine parameters and forms, such as SVM. Therefore, as a preliminary study, BP neural network is chosen as the final model. However, with the deepening of research and attempts, the application of other types of non-linear models may obtain better prediction results. It is suggested that other models be tried in the next study. The relationship between macro factors is closer to non-linearity. The main purpose of applying linear model analysis is to provide more empirical information for modeling from the perspective of correlation. The model prediction results also verify that this method may have a good reference significance for improving the accuracy of the model. 5. Conclusions Starting with the analysis of the data characteristics of the number of coal mine accidents in China, the early warning model was established, and the conclusions are as follows.
Fig. 7. Prediction results of BP neural network.
input variables, thus determining the input structure of the model. It may provide a new way for solving similar problems. However, the research of the VAR model is based on the linear model, and the complex relationship between the macro factors is more approximate to be nonlinear. As a result, the early warning model with the nonlinear relationship or the complex system dynamic relationship as the core will be the direction in the future. There are some defects in BP neural network itself, especially the over-fitting problem, which affects the application of BP neural network. However, BP neural network has good performance in fitting the non-linear relationship, especially when the influence relationship between factors affecting coal mine accidents is not clear. So it is difficult
(1) The characteristics of the number of major coal mine accidents data is: with a downward trend, small samples, relatively small numerical value and large fluctuations. (2) Five variable PID, PMI, EI, PPI, RMPPI and the lag data of the number of coal mine accidents are considered to have a complex impact on the macro value of the number of coal mine accidents. (3) Data with properties of downward trends small samples, relatively small numerical value, large fluctuations, can be predicted well by the BP model with the input structure determined by VAR model. (4) The coal mine accident early warning model, the BP model with the
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input structure determined by VAR model, obtains the prediction results that has the correlation coefficient of 0.95368.
resources. Resour. Pol. 53, 317–327. Li, G., Yang, H.X., Yuan, C.M., et al., 2016. A catastrophic aluminium-alloy dust explosion in China[J]. J. Loss Prev. Process Ind. 39, 121–130. Liu, T., 2010. Range far your eye over long vistas——consideration on the safety situation and evolution tendency in the recent and medium term. J. Saf. Sci. Technol. 6 (6), 5–10. Qi, G., Wang, D., Chen, Y., et al., 2014. The application of kinetics based simulation method in thermal risk prediction of coal[J]. J. Loss Prev. Process Ind. 29, 22–29. Ren, Z., Liu, T., Liu, G., 2008. The quantitative analysis of the relationship between the trend of industrial accidents and the intervention of the safety policy in China. J. Saf. Sci. Technol. 4 (4), 68–72. Tang, J., Jiang, C., Chen, Y., et al., 2016. Line prediction technology for forecasting coal and gas outbursts during coal roadway tunneling. J. Nat. Gas Sci. Eng. 34, 412–418. Tang, E., Peng, C., 2017. A macro- and microeconomic analysis of coal production in China. Resour. Pol. 51, 234–242. Wang, H., Zhu, H., 2008. On the major economic and social factors affecting work safety in China. China Coal 34 (6), 84–87. Wei, C., Xu, M., Sun, J., et al., 2011. Coal mine gas emission gray dynamic prediction. Procedia Eng. 26, 1157–1167. Zhang, S., 2011. Safety and socio-economic development of Sichuan Province. Soft Science 25 (6), 123–126. Zhao, D., He, X., 2013. Multiple regression analysis on occupational safety and economic and society in China. J. Saf. Sci. Technol. 9 (10), 66–70. Zhao, L., Tao, Y., 2012. Factors influencing urban work safety and its prediction and analysis: a case study of Shanghai. China Saf. Sci. J. 22 (6), 8–13.
Acknowledgement The project is funded by the National Key Research and Development Program of China (2017YFC0804901). References Guo, J., An, Q., Shen Chan, J., Du, G., et al., 2016. China Production Safety Almanac (2015) (Chinese Edition). Coal Industry Press. He, X., Song, L., Nie, B., 2008. Basic characteristics of occupational safety in China. China Saf. Sci. J. 18 (1), 5–13. Huang, S., Zhou, X., Zhang, B., 2005. Multiple regression analysis on occupational safety and economic and social development. J. China Coal Soc. 30 (5), 580–584. Janusz, A., Grzegorowski, M., Michalak, M., et al., 2017. Predicting seismic events in coal mines based on underground sensor measurements. Eng. Appl. Artif. Intell. 64, 83–94. Kong, L., Tian, H., 2007. Effect of socio-economic development index on work safety and its grey correlation analysis. China Saf. Sci. J. 17 (1), 46–50. Li, C.M., Nie, R., 2017. An evaluating system for scientific mining of China's coal
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An empirical study of early warning model on the number of coal mine accidents in China
T
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Qing Liu, Jian Liu , Jinxin Gao, Jingjing Wang, Jing Han School of Civil and Environmental Engineering, University of Science and Technology Beijing, No. 30 Xueyuan Road, Haidian District, Beijing 100083, PR China
A R T I C LE I N FO
A B S T R A C T
Keywords: Coal mine accidents BP model VAR model Early warning
Coal mining is an important energy industry as well as high risk industry with high accident rate. The objective of this study is to establish an early warning model for coal mine accidents. Based on the accident data in the recent 11 years, it is concluded that the coal mine accidents data has the characteristics of large fluctuations, small sample size, small value and large numerical nonlinear characteristics. After literature consulting, data analysis and variable comparison, Policy intervention degree (PID), Manufacturing Purchasing Managers' Index (PMI), Producer Price Index for Industrial Products (PPI), Main raw material purchase price index (RMPPI), Employment index (EI) are selected as the auxiliary variables to construct the early warning model. VAR model is applied to determine the model structure. Results show that BP neural network model is suitable for the prediction of the number of coal mine accident.
1. Introduction Coal mining is the most important energy industry as well as one of the highest riskindustries in China. In 2016, approximately 77% of energy production was generated by coal in China (Tang and Peng, 2017). Meanwhile, there were 249 coal mine accidents in coal industry resulting in 526 deaths (Guo et al., 2015). There is an inherent risk in coal mine, and because of the huge amount of employee involved in and the serous accident results, having a great influence on society. With the development of economic and the progress of coal mining technology, coal mechanization degree and efficiency of raw coal are unceasingly increasing, and the mine production capacity is constant enhancing, but the development is unbalanced and inadequate. Complex mining conditions of coal mine and large demand of personnel cause the working conditions in coal mine varied and dangerous, bringing a great challenge for safety management. Thus it attracts more and more attention on safety production strengthening and accident prevention. Traditional accident treatments are no longer able to satisfy the expectation and need on safety management, and pre-accident treatments are getting more and more important. Accident earlywarning model plays a more important role in safety management for its help to pre-accident treatments. The foundation of accident early warning is the causality and statistical inevitability of accidents, which can be seen as the comprehensive results of the recurrence of time trend and other disturbance
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factors. As a result, making full use of the internal mechanism of the accident and the accumulate relevant information is the key to get reliable early warning results. Many existing works dealing with accident prediction are devoted to taking qualitative approaches in order to understand the potential reasons causing coal mine accidents, such as surveys (Li et al., 2017), interviews (Li et al., 2016), or simulations (Qi et al., 2014), rather than collecting real field data. In recent years, statistical methods have been applied to describe the trend of accident. Tang et al (2016) deduced a theoretical line prediction formula for calculating the amount of gas released from boreholes during drilling, with the parameters of ground stress, gas pressure, and coal strength around the borehole during drilling, which could accurately reflect the outburst hazard in front of working faces. Janusz et al. (2017) compared the prediction ability between expert methods which serve as a standard in the coal mining industry and state-of-the-art machine learning methods with high-dimensional time series data, and they both have their own advantages. Wei et al. (2011) found new information models had a better prediction effect over grey prediction models and metabolism models to realize the dynamics of coal mine gas emission projections, which was verified and analyzed by the example of the mine in Hegang. But there are few studies on early warning of the total amount of coal mine accidents. At present, some safety production information collection and processing systems have been equipped with the function of preliminary accident number estimates with accident related index,
Corresponding author. E-mail address: [email protected] (J. Liu).
https://doi.org/10.1016/j.ssci.2019.104559 Received 15 October 2018; Received in revised form 28 October 2019; Accepted 24 November 2019 0925-7535/ © 2019 Published by Elsevier Ltd.
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but the precision of the early warning models could be further improved. This paper proposes a combination model to realize the early warning of the number of coal mine accidents. For quantitative forecasting, it is necessary to infer a forecasting model based on historical data. Linear models have outstanding advantages among forecasting models. On one hand, linear models are easy to understand the correlation analysis between factors and combine the existing qualitative experience. On the other hand, based on strict statistical analysis framework, they can better verify the theoretical assumptions. The disadvantage of linear models is that it is difficult for them to express complex relationships, and the prediction accuracy is relatively low. Nonlinear models based on deterministic forms, on the contrary, can express complex correlations. But due to the limitation of the amount of real data, it is often difficult for nonlinear models to obtain an ideal model form. Nonlinear models based on machine learning have prominent advantages in the expression of complex correlation and the determination of model form, while the analysis of the relationship between factors is not mature enough. Therefore, a combination model is established by combining linear models with BP neural network model.
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The number of accidents is a very important accident statistical index, which can represent the occurrence trend of the accident, while the number of deaths in each accident still has a certain randomness. Therefore, comparing to the number of accident death toll, once the severity level of the accident is determined, the randomness of the time series of coal mine accident number is relatively smaller, and as a result it can represent the changes of safety production situation in a certain area in a more comprehensive way. The major accident data in this paper was collected from the web of State Administration of Work Safety of China and the web National Bureau of Statistics of the People’s Republic of China. Major accidents here are accidents caused 3 people or more died, or 10 people or more seriously injured, or more than 50 million yuan direct economic losses. Fig. 1 shows the major accident time series of coal mine accident. The number of major accidents is more in amount than the number of severe accidents, which has a more obvious statistical law over the later. Due to the complete records of major accidents, there are less problems of data missing and distorted. Therefore, the reliability and authenticity of the accident data are higher than that of the total accidents. As a result, the number of coal mine major accidents is chosen as the study subject in this paper. At present, limited by historical records of accident in the coal mine
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Heinrich accident causal chain theory shows that the occurrence of accidents is caused by a series of events that have a causal relationship. Coal mines have varied mining conditions, a wide area distribution, different processes, leading to the different status, which means it is hard to fully grasp the direct and indirect causes of coal mine accidents. But long-term practice and research has allowed us to get a lot of information about the factors that are related to the accident of coal mine, which makes it possible to search for the important cause of it. Many scholars (Huang et al, 2005, Zhao and Tao, 2012, Zhang, 2011, He and Nie, 2008, Wang, 2008, Zhao and He, 2013, Kong and Tian, 2007, Ren et al, 2008, Liu, 2010) have done long-term research on the macro factors affected the coal mine accident, which are mainly including four aspects: the indexes of economic development, the indexes of social structure, the indexes of population quality and the indexes of policy intervention. The indexes of social structure and the indexes of population quality data statistic once a year or once a quarter, data’s monthly changes couldn’t be obtained. What’s more, the range of data changes from month to month is very small which can be neglected. Therefore, only fixed asset investment growth rate is considered as a factor, presented
The number of coal mine accident
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2.2. Auxiliary indexes selected
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industry in China, the coal mine accidents data from 2006 to 2016 are suitable as the study subject for its consistent statistical standard and good continuity. To improve the accuracy and the sensitivity of the early warning, and enhance the practicability of the early warning results, monthly data is selected as the basis in this paper. As shown in Fig. 2, accident time series has the properties of downward trend, relatively large fluctuations, a sharp graphics, relatively small numerical value, data fluctuation range from 9 to 189. There isn’t obvious periodicity in the series, and the fluctuation range of which is gradually decreasing. In most months, the number of major coal mine accidents is less than 150. There are only 2 months in which the number of major coal mine accidents is larger than 180. In most months, the number of coal mine accidents is no more than 70. The mean value of the number of major coal mine accidents is 81.04, and the standard deviation of it is 49.12. The coefficient of variation is the ratio of the standard deviation and the mean. The coefficient of variation of the number of major coal mine accidents is 0.6061, which indicates that time series has a feature of big dispersion. Skewness value is 0.3667 > 0, indicating that the data has a positive deviation, also known as right skewness, which shows that the accident number distribution is concentrated in a small numerical region. The kurtosis value is 1.9615 < 3, indicating a lack of kurtosis, shows there are extreme values. The overall characteristics of the number of accidents data is: with a downward trend, small samples, large fluctuations.
2.1. The number of coal mine accident
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Fig. 2. Histogram of the number of coal mine accident.
2. Study subject
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Fig. 1. The number of coal mine accident. 2
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as Completed in Fixed Assets, Accumulated, and the rest are not contained as auxiliary indexes for predicting model. At the same time, in order to reflect the social structure changes, use Employee Index (EI) as the social structure indicator. The influence factors directly related to coal mine accidents such as coal seam occurrence conditions, ignition tendency of coal mine, production method of coal mine, mining velocity and so on, are not considered. Because they vary greatly from region to region, and it is difficult to quantify the unified index as well as to obtain relevant data, coupled with the fact that influence of the macroscopic number of accidents can’ t be estimated. Considering the number of coal mine accidents is related to safety investment, and enterprise safety input is closely related to the profit of its production, the Main raw material purchase price index (RMPPI), Producer Price Index for Industrial Products (PPI) are chosen as auxiliary indexes. Because the purpose of paper is to predict monthly coal mine accident number, we mainly consider indexes which are in macro level, monthly data available and having access to complete data from 2006 to 2015. In order to meet these demands, there is a need to screening and replace the index of predicting according to the objective substitution. Table 1 shows the selected indicators. Factors screened out are as follows: RMPPI, PMI, PPI, EI, PID.
Table 2 Results of unit root test.
(2)
−1.154673 −8.985810 −3.244707 −3.494752 −2.913454 −3.961365 −6.967925
0.6922 0.0000 0.0197 0.0096 0.0465 0.0022 0.0000
VAR model is a kind of econometric model, which describes the linear relationship between multiple time series. It extends the single variable autoregressive model (AR model) through allowing multiple endogenous variables. All variables in VAR enter the model in the same way: each variable has an equation, explain its evolution with its own lag value, the lag value of other model variables and the error term. In recent years, VAR model has been widely used to explore the interaction among economic variables on macroeconomic issues in econometric analysis. For VAR model, the mathematical expression is shown below.
yt = B0 + B1 yt − 1 + B2 yt − 2 + ⋯+Bp yt − p + ut
(3)
where t represents the time step; z t − 1is the value of 1 lag of the test data; Δz t is the difference between test data in time step t and time step t-1; μ represents the displacement term; α andρ are the coefficient of t and Table 1 The Auxiliary index of coal mine accident prediction. Category
Initial index
Selected index
Indexes of economic development
Gross Domestic Product (GDP), per capita gross national product (GNP)
Indexes of social structure
Agricultural output value accounted for the proportion of GDP, the Secondary industrial output value accounted for the proportion of GDP, the Tertiary industrial output value accounted for the proportion of GDP, Research investment accounted for the proportion of GDP, Fixed asset investment growth rate, Exports of goods and services account for the proportion of GDP, education funds accounted for the proportion of GDP Enrollment rate of middle school students, Natural Growth Rate, infant Death rate policy intervention degree
Main raw material purchase price index (RMPPI), Manufacturing Purchasing Managers' Index (PMI), Producer Price Index for Industrial Products (PPI) Employment index (EI)
Indexes of population quality indexes of policy intervention
(4)
where yt represents the value of all the variables in time step t; yt − p represents the value of p lag of the variables; Bp represents the coefficient of p lag of the yt − p ; t represents the time step. ut has a normal distribution with the mean value of 0 and variance of δ 2 . Due to the presence of contemporaneous correlation, any change of one explanatory variable will likely trigger the changes of other explanatory variables and thus jointly influence the response variables. Quantifying the contribution of each individual explanatory variable to the response variable is very important to build the structure of prediction model. The impulse response function is able to help to study the dynamic and convoluted relationship between variables in a clearer manner. When the disturbance term of an explanatory variable plus one-unit of standard deviation, other disturbance terms of explanatory variables still remain constant, and the corresponding value of the explanatory variable is called impulse response function. Impulse response explains the contemporaneous correlation and lagged dynamic influence
where x is the raw data; X is the normalized data; x max and x min are the maximum and minimum value in the raw data. Before building VAR model, it is important to confirm the stationarity of data sets and the existence of long-run correlation among the input data. When data is non-stationary, it might cause spurious regressions, leading to untreatable results. The formulas of the stationarity are as follows.
Δz t = μ + αt + ρz t − 1 + ut , z 0 = 0, ut ~N (0, δ 2)
ACCI PID PMI EI PPI RMPPI DACCI
3.2. VAR model
In order to eliminate the order of magnitude difference between inputs and output, the normalization method was used to preprocess each input and output before network learning. The general formulas of normalization and anti-normalization are described below.
x = x min + X × (x max − x min )
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z t − 1. If ρ = 0, it means that the data set y is unstable. If ρ = 1, then it means that the data set y is stable. Differential processing is performed on the unstable sequence before normalized again. The results are in Table 2.
3.1. Data pre-processing
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t-Statistic
Except variable ACCI, all other variables passed the unit root test.
3. Model development
x − x min X= x max − x min
Variable
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Table 3 Tested parameter settings of BP model.
between variables, enable us to get an estimate about how long and how much would one variable affect another. 3.3. BP network BP (Back Propagation) neural network has the ability of selflearning, self-organizing, self-adaptive and powerful nonlinear mapping. So it is often applied to the study of uncertain problem involving complex causality. BP neural network is one of the most widely used neural network models at present. Itcan learn and store a large number of input-output mapping relations without prior revealing the mathematical equations describing the mapping relationship. A three-layer BP neural network was developed in this study for its sufficient ability to approximate any continuous nonlinear functions. Its architecture is shown in Fig. 3. BP neural network consists of the input layer, hidden layer and output layer. The rule of learning is using the gradient descent method, to adjust the weights and thresholds of the network through reverse propagation, and making the sum of the square sum of the error between the actual output of the neural network and the desired output minimum. The learning process of the neural network includes two stages: forward propagation and backward propagation. In the forward propagation process, the transmission information is processed from the hidden layer by layer to the output layer, and the state of each layer of neurons only affects the state of the next layer of neurons. In the backward propagation process, the difference between the ideal output and the actual output is reversed by the connection path, and the weights of the neurons in each layer are adjusted by the gradient descent method.
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The established BP model has an input of 24 variables, the fitting results are shown in Fig. 6. The number of hidden layer neurons was determined by empirical formula as below, and 10 neurons of Hidden layer were selected. h represents the number of hidden layer neurons, m represents the number of input layer neurons, n represents the number of output layer neurons, a represents a constant of 0–10.
h=
m+n +a
(7)
The value R was applied to measure the goodness of fit. The R value of the training set is 0.97585, while that of validation set and testing set are 0.89832 and 0.93708 respectively, which suggests that the BP model has a good fitness of the data set to predict. In the prediction results, it can be seen that the training goodness of fit is obviously higher than the test and verification goodness of fit, which is in line with the general prediction situation, indicating that the selected model was still not the optimal model. According to the ideal situation, the number of the three should be completely equal, which indicates that the prediction model is more or less over-fitting. However, it can be seen that the goodness of fit has reached 0.90, the representative model has achieved good results, and the goodness of fit has also reached 0.94 as expected, indicating that the model is worthy
(5)
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The number of neurons in hidden layer Learning rate The number of iterations
The VAR models with different lag orders are estimated based on five criteria of LogL, LR, FPE, AIC, SC and HQ, and the estimated results of are shown in Table 4. When the lag order is 2 or 11, there are two principles with optimal result. The VAR model was established by selecting the lag order of 2 by the simplest principle of the model. The result of inverse roots of AR characteristic polynomial for the established VAR model is shown in Fig. 4. The absolute value of the characteristic root of the characteristic equation corresponding to the VAR model falls within the unit circle, which means the value of it is less than 1, showing the VAR model is stationary. The impulse response of the difference of the number of major coal mine accidents in China is shown in Fig. 5. The variable DACCI refers to the difference of the number of major coal mine accidents after preprocessing. The other variables refer to preprocessing results of variables corresponds to its name. When the disturbance term of the variable PID, PMI, EI, PPI, RMPPI plus one-unit of standard deviation, other disturbance terms of explanatory variables still remain constant, the variable DACCI is affected in 1–4 times step lag. As a result, 4 times step lag was selected as the structure of input of BP model.
∑ (x i ̂ − x i )2 R=
L n ep
4.1. Model structure determination
VAR model in this study was developed with all the variables after data pre-processing. The suitable VAR lag order was selected to establish a suitable VAR model based on the optimal results of 5 criteria such as AIC and SC. The impulse response analysis of the VAR model was made to determine the lag order of each auxiliary variable and the BP model was established based on it. BP model in this study was developed using 125 sets of experimental data. During the learning process, 88 data sets were selected as the training set and 19 data sets were selected as the validation set. During the testing process, the rest 18 data sets were used as the test set. Furthermore, the model performance was evaluated by the correlation coefficient(R) and root mean squared error (RMSE) which could be calculated as N
Levels
4. Results and discussion
3.4. Model building
∑
Definition
where N is the number of all samples; x is the experimental target output; x ̂ is the neural network prediction output after the anti-normalization process. x¯ is the mean value of target output. The hidden neuron number and population size are important parameters influencing the model performance, but no general formula has been found to determine these two parameters currently. Therefore, in order to determine appropriate model parameters, this study experimented with various parameter settings (see Table 3), using MATLAB R2016b.
Fig. 3. The architecture of three-layer BP neural network.
RMSE =
Parameter
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Table 4 VAR lag order selection criteria. Lag
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AIC
SC
HQ
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362.4853 770.9978 901.1018 932.2710 964.2144 1001.455 1034.659 1076.169 1115.959 1161.646 1208.242 1334.620 1385.852
NA 768.9647 231.7821 52.38509 50.46526 55.07798 45.76058 53.02082 46.81163 49.14254 45.42147 110.4478* 39.60831
1.01e−10 1.93e−13 3.98e−14 4.36e−14 4.77e−14 4.84e−14 5.36e−14 5.28e−14 5.51e−14 5.40e−14 5.46e−14 1.53e−14* 1.63e−14
−5.991349 −12.25206 −13.83364 −13.75245 −13.68428 −13.70512 −13.65813 −13.75073 −13.81443 −13.97724 −14.15533 −15.67428 −15.93029*
−5.851225 −11.27120 −12.01203* −11.09010 −10.18118 −9.361280 −8.473550 −7.725407 −6.948360 −6.270427 −5.607770 −6.285981 −5.701246
−5.934449 −11.85376 −13.09395* −12.67136 −12.26178 −11.94122 −11.55284 −11.30404 −11.02634 −10.84775 −10.68443 −11.86199 −11.77660
Inverse Roots of AR Characteristic Polynomial
of affirmation in terms of prediction ability. The prediction result is shown in Fig. 7. The prediction results have accurately reflected the actual fluctuations, which suggests that the BP model have a good early warning ability. The RMSE of the established BP model is 7.80, which is far less than the average number of relative monthly data.
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4.3. Discussion
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In this paper, the VAR model is established to determine the lag interval of the input variables, based on which the structure of the input was determined. The number of major coal mine accidents in the next month is predicted by the BP neural network, and the early warning function of the macroscopic coal mine accidents is realized. Macroeconomic, social and policy variables are considered in the established model, thus representing their impact on coal mine accidents to a certain extent. The results show that the model has a good fitting effect. This largely attributes to the nonlinear relation between the relative variables of macro economy, society and policy and the number of major coal mine accidents, and the BP neural network’s advantages in dealing with the nonlinear relation. In the research of macro prediction and macro early warning, due to the factors influencing the accidents in a complex way, the choice of lag time for these data is a very important foundation for model establishment. This article applied VAR model to select the lag period of
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Fig. 4. The result of inverse roots of AR characteristic polynomial.
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Fig. 5. The impulse response of the difference of the number of major coal mine accident in China. 5
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Fig. 6. The fitting result of BP neural network.
to apply the model which depends more on experience to determine parameters and forms, such as SVM. Therefore, as a preliminary study, BP neural network is chosen as the final model. However, with the deepening of research and attempts, the application of other types of non-linear models may obtain better prediction results. It is suggested that other models be tried in the next study. The relationship between macro factors is closer to non-linearity. The main purpose of applying linear model analysis is to provide more empirical information for modeling from the perspective of correlation. The model prediction results also verify that this method may have a good reference significance for improving the accuracy of the model. 5. Conclusions Starting with the analysis of the data characteristics of the number of coal mine accidents in China, the early warning model was established, and the conclusions are as follows.
Fig. 7. Prediction results of BP neural network.
input variables, thus determining the input structure of the model. It may provide a new way for solving similar problems. However, the research of the VAR model is based on the linear model, and the complex relationship between the macro factors is more approximate to be nonlinear. As a result, the early warning model with the nonlinear relationship or the complex system dynamic relationship as the core will be the direction in the future. There are some defects in BP neural network itself, especially the over-fitting problem, which affects the application of BP neural network. However, BP neural network has good performance in fitting the non-linear relationship, especially when the influence relationship between factors affecting coal mine accidents is not clear. So it is difficult
(1) The characteristics of the number of major coal mine accidents data is: with a downward trend, small samples, relatively small numerical value and large fluctuations. (2) Five variable PID, PMI, EI, PPI, RMPPI and the lag data of the number of coal mine accidents are considered to have a complex impact on the macro value of the number of coal mine accidents. (3) Data with properties of downward trends small samples, relatively small numerical value, large fluctuations, can be predicted well by the BP model with the input structure determined by VAR model. (4) The coal mine accident early warning model, the BP model with the
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input structure determined by VAR model, obtains the prediction results that has the correlation coefficient of 0.95368.
resources. Resour. Pol. 53, 317–327. Li, G., Yang, H.X., Yuan, C.M., et al., 2016. A catastrophic aluminium-alloy dust explosion in China[J]. J. Loss Prev. Process Ind. 39, 121–130. Liu, T., 2010. Range far your eye over long vistas——consideration on the safety situation and evolution tendency in the recent and medium term. J. Saf. Sci. Technol. 6 (6), 5–10. Qi, G., Wang, D., Chen, Y., et al., 2014. The application of kinetics based simulation method in thermal risk prediction of coal[J]. J. Loss Prev. Process Ind. 29, 22–29. Ren, Z., Liu, T., Liu, G., 2008. The quantitative analysis of the relationship between the trend of industrial accidents and the intervention of the safety policy in China. J. Saf. Sci. Technol. 4 (4), 68–72. Tang, J., Jiang, C., Chen, Y., et al., 2016. Line prediction technology for forecasting coal and gas outbursts during coal roadway tunneling. J. Nat. Gas Sci. Eng. 34, 412–418. Tang, E., Peng, C., 2017. A macro- and microeconomic analysis of coal production in China. Resour. Pol. 51, 234–242. Wang, H., Zhu, H., 2008. On the major economic and social factors affecting work safety in China. China Coal 34 (6), 84–87. Wei, C., Xu, M., Sun, J., et al., 2011. Coal mine gas emission gray dynamic prediction. Procedia Eng. 26, 1157–1167. Zhang, S., 2011. Safety and socio-economic development of Sichuan Province. Soft Science 25 (6), 123–126. Zhao, D., He, X., 2013. Multiple regression analysis on occupational safety and economic and society in China. J. Saf. Sci. Technol. 9 (10), 66–70. Zhao, L., Tao, Y., 2012. Factors influencing urban work safety and its prediction and analysis: a case study of Shanghai. China Saf. Sci. J. 22 (6), 8–13.
Acknowledgement The project is funded by the National Key Research and Development Program of China (2017YFC0804901). References Guo, J., An, Q., Shen Chan, J., Du, G., et al., 2016. China Production Safety Almanac (2015) (Chinese Edition). Coal Industry Press. He, X., Song, L., Nie, B., 2008. Basic characteristics of occupational safety in China. China Saf. Sci. J. 18 (1), 5–13. Huang, S., Zhou, X., Zhang, B., 2005. Multiple regression analysis on occupational safety and economic and social development. J. China Coal Soc. 30 (5), 580–584. Janusz, A., Grzegorowski, M., Michalak, M., et al., 2017. Predicting seismic events in coal mines based on underground sensor measurements. Eng. Appl. Artif. Intell. 64, 83–94. Kong, L., Tian, H., 2007. Effect of socio-economic development index on work safety and its grey correlation analysis. China Saf. Sci. J. 17 (1), 46–50. Li, C.M., Nie, R., 2017. An evaluating system for scientific mining of China's coal
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