Study on Early-Warning Assessment in Chinese Coal Mine Safety Based on
Genetic Neural Networks
Yong-wen Ju, Li-xia Qi, Qian-li Sun
School of Management and Economics, North China University of Water Resources and Electric Power
Zhengzhou, P.R.China
(juyongwen@126.com,
yx1161@126.com, sunqianli1987@126.com)
Abstract - The early-warning and pre-control process to
recognize potential safety hazard of coal mine based on
characteristics of production safety is put forwards in the
paper. The warning evaluation index system of coal mine
safety which influenced by human, machine and equipment,
environment, management and information is established.
Then it conducted an empirical study by using an evaluation
method of neural network based on genetic algorithm.
Evidence shows that the method has better adaptability and
high accuracy by combining with an example in supporting
persistent effect mechanism for the safety production of coal
mine.
Keywords - Coal mine safety, genetic algorithm, neural
network, early-warning assessment
I
INTRODUCTION
At present, an increase rate in China coal mine
demand annually is about 10%, which promotes the coal
industry development and produces kinds of coal
accidents at the same time. According to the statistics,
China is one of the countries that have the highest
frequency of coal mine accidents.
The current supervision mechanism of coal mine
safety in China is based on the emergency plan
management, and the early warning mechanism had not
been really set up. It is necessary to build long-term
prevention mechanism combined with the theory and
evaluation method of coal mine safety and early warning
to monitor, diagnose, control and correct production
activities of coal mine. It could provide theoretical basis
and technical support for preventing and reducing coal
mine accidents.
II
LITERATURE REVIEW
Domestic and foreign scholars have carried on the
active exploration and research in the assessment method
of coal mine safety. The main assessment methods include:
1) Fuzzy comprehensive evaluation: Ding Xia-jun (2004)
[1]
, Sun Jia (2005) [2],Gao Wen-hua (2008) [3], Sun
Jian-hua(2009) [4] built the index system and estimate the
safety of coal mine by fuzzy comprehensive evaluation,
they obtain the conclusion consistent with actual situation
____________________
National social science fund projects（12CGL101）;
Graduate students’ education innovation fund project plan of North China
University of Water Resources and Electric Power（YK2011-03）;
Henan province social science planning project (2009BSH005)
by the quantitative evaluation. 2)Grey relation method: Xu
Yi-Yong(2003) [5],Cao Shu-gang(2007) [6],Fu Yong-shuai
（2009）[7]established the evaluation index system based
on the reality of coal mine production. Gray correlation
analysis is used to evaluate the coal mine safety, and the
different levels of security evaluation results are induced
accordingly. 3) Unascertained mathematics evaluation
method: Yan Le-lin(2004) [8] built the index system,
confidence identification criteria and rating criteria based
on unascertained mathematics theory, the safety indictor
of coal mine is analyzed through building the
unascertained measure model. 4) Neural network
evaluation method :Huang Hui-yu(2007) [9],Zhou
[10]
Zhong-ke(2011)
,Gao
Xiao-xu(2011) [11],Ding
[12]
Bao-cheng(2011)
built the index system and estimate
the safety of coal mine by neural network .Then the
practicality and effectiveness of the model are verified.
From what we have analyzed above, the assessment
methods that are used commonly include the AHP, fuzzy
comprehensive evaluation, grey relation method,
unascertained mathematics evaluation method and neural
network evaluation. However, these methods are short of
the ability of self-learning, it is difficult to get rid of the
subjective uncertainty and understanding of the ambiguity
in the decision-making process. The neural network
evaluation could avoid the defect, but there are some
inadequacies such as slow convergence velocity and
potential trapping into local search. Genetic algorithm can
find the global optimum and have a good robustness.
Therefore, it has fast convergence velocity and strong
self-learning ability through the combination of genetic
algorithms and neural networks. In this paper, the coal
mine production safety is assessed by using an evaluation
method of neural network based on genetic algorithm.
The “human-machine- environment” evaluation
index system of coal mine safety is established based on
the accident causing theory. Zhang Yu-lin(2008) [13],Xu
Yang(2009)[14] consider that the accident was caused due to
unsafe state of human, machine and environment. Sun
Jian-hua(2009)[4] think that the coal mine safety evaluation
index system should include the factors of human,
legislation, machine, engineering technology and disaster
prevention.
Security information management plays an important
role in the process of coal mine production. Coal mine
safety is affected because of backward informatization
construction in coal mine enterprise. The author thinks
that the safety production of coal mine is influenced by the
factors of human, machine, environment, management
and information. The accident was the interaction of these
factors’ defects. So, “human-machine-environmentmanagement- information” evaluation index system of
coal mine safety is established in this paper.
So, the coal mine safety is assessed by genetic neural
network based on the evaluation index system. The
genetic neural network has been applied in evaluation of
the corporation’ core competence and risk project. But
there is no related research in the safety assessment of coal
mine. The index system and assessment methods built in
this paper have great realistic meanings and long-term
meanings to enhance early-warning and evaluation theory
of coal mine safety.
III
EARLY WARNING ASSESSMENT INDEX
SYSTEM OF COAL MINE
The index system of “people-machine-environmentmanagement-information” is shown in table 1.
IV
The major part of genetic neural network is to
optimize the weights of network. First it finds the optimal
solution by genetic algorithm; it can narrow down the
searching range. Then it will use the BP neural network to
find the optimal solution[15]. The specific steps are as
follows:
A. Determine the network structure of the model
The layers of neural network include input, hidden
and output layers.
1) Set the input of the network: To make the original
data more suitable for neural network through pretreating.
The quantitative indicators should be normalized, and the
qualitative indexes should be quantified. The number of
network input nodes are equal to the index number of
evaluation index system. Therefore, the input nodes in this
paper are 29.
2) Determine the output nodes and hidden layer
nodes：The output nodes should be corresponded to the
early warning assessment. The output nodes are 5 and the
hidden layer nodes are 15 in this paper based on the
experience formula. The corresponding output results alert
are shown in table 2
EMPIRICAL ANALYSIS BASED ON EARLY
WARNING MODEL OF COAL MINE
TABLE I Early warning assessment index system of coal mine
Rule layer
human factor
(X1)
machine factor
(Y1)
The factors of
geological
environment(Z1)
Environment
factor
(Z)
factors of mine
disaster (Z2)
factors of work
environment (Z3)
Management factor(U1)
information factor (V1)
Index layer
Violation rate of employees (X11)
Average level of education (X12)
training time per month(X13)
level of mining mechanization (Y11)
the rate of support equipment at good condition (Y12)
the rate of ventilation equipment at good condition (Y13)
the rate of dust-proof equipment at good condition (Y14)
the rate of fire-fighting equipment at good condition (Y15)
the rate of drainage equipment at good condition (Y16)
the rate of lifting equipment at good condition (Y17)
the rate of mechanical and electrical equipment good condition (Y18)
the rate of transport equipment at good condition (Y19)
the rate of gas drainage equipment at good condition (Y110)
the average fault throw (Z11)
number of fault bars per unit area (Z12)
coal thickness coefficient of fault (Z13)
The degree of difficulty of controlling the roof (Z14)
spontaneous combustion period (Z21)
coal dust explosion index (Z22)
average Gas Emission (Z23)
mining surface rich water coefficient (Z24)
pass rate of controlling dust pollution (Z31)
pass rate of controlling sound pollution (Z32)
degree of perfection on management system (U11)
capacity of emergency rescue (U12)
timeliness and effectiveness of management (U13)
degree of informatization (V11)
capacity of information recognition(V12)
capacity of information processing(V13)
TABLEⅡOutput result of neural network correspond to the alert
10000
01000
00100
highest
higher
medium
B. Optimize weights of network by Genetic algorithm
The steps which optimize weights of network are as
follows:
2) Determine encoding mode and evaluation function:
Calculate selection possibility of each individual and
select individual which have the biggest sufficiency value
for the next-generation [16].
3) Operate selection, crossover and mutation:
Population is operated by stochastic universal sampling,
two-point crossover and uniform mutation.
4) Output the individual with best fitness degree
value: Select the neural network which has minimum
errors and thresholds to train until the error reaches the
precision. Set the termination condition and the error is
less than 0.0001.
C. Empirical analysis of the model
In order to verify the feasibility and practicality of the
model, network training is operated with monitoring data
of five selected coal mining enterprises in four quarters in
2011. The error curve of network training is shown in
figure 1. The training result shows that: the network
training is completed because the network error is less than
0.0001 after 201 times of training. Select five samples to
test the network and it is shown in Table 3. The test results
are consistent with the practical situation. Therefore, it is
proved that the model that build in this paper have right
evaluation to the safety situation of coal mine, and it
provides a scientific basis for policy-makers to judge the
safety conditions and formulate the countermeasures.
V
Performance is 9.69382e-005, Goal is 0.0001
0
-1
10
-2
10
-3
10
-4
10
CONCLUSIONS
0
X12
0.80
0.75
0.43
0.52
0.25
X13
0.60
0.87
0.27
0.40
0.13
00001
lowest
10
The early-warning assessment system in coal mine
safety is a significant process that can prevent and control
the accident. This paper analyzes and identifies the
X11
1.00
1.00
0.88
1.00
0.58
00010
lower
1) Population initialization: A combination of the
initialization function and the random function is chosen to
select the initial population. Cross-scale, crossover
probability and mutation probability are included.
potential accidents and risk factors which may affect
safety production, and build the genetic neural networks;
it is proved that the model built in this paper has right
evaluation to the safety situation of coal mine. Therefore,
the model can assess the safety production in coal mine,
and warn the weak in the production. Coal production
could enter the safe orbit through timely adjustment in
production by administrator.
Compared with previous studies, the contributions of
this paper include：Firstly, the factors contributed coal
mine safety production is analyzed comprehensively
through the early warning index system including the
information factors. Secondly, genetic neural network is
able to achieve ideal empirical results when assessing the
coal mine safety.
Training-Blue Goal-Black
Output result
Safe alert
Y11
0.40
0.43
0.28
0.51
0.38
Y12
0.60
0.80
0.60
0.50
0.77
Y13
1.00
1.00
0.60
0.87
0.47
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40
60
80
100 120
201 Epochs
140
160
180
200
Fig.1. Training curve of BP algorithm
TABLE Ⅲ The test sample of model
Y14
Y15
Y16
Y17
…
0.80
0.87
0.53
1.00
…
0.87
0.33
0.60
0.00
…
0.80
0.13
0.13
0.27
…
0.87
0.60
0.60
0.60
…
0.33
0.07
0.00
0.00
…
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