Citation: Wang, X.; Xu, N.; Meng, X.;
Chang, H. Prediction of Gas
Concentration Based on
LSTM-LightGBM Variable Weight
Combination Model. Energies 2022,
15, 827. https://doi.org/
10.3390/en15030827
Academic Editors:
Luis Hernández-Callejo,
Adam Smoli´nski, Sara
Gallardo Saavedra and
Sergio Nesmachnow
Received: 15 November 2021
Accepted: 19 January 2022
Published: 24 January 2022
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Article
Prediction of Gas Concentration Based on LSTM-LightGBM
Variable Weight Combination Model
Xiangqian Wang *, Ningke Xu, Xiangrui Meng and Haoqian Chang
School of Computer Science and Technology, Anhui University of Science & Technology, Huainan 232000, China;
nkxu999@gmail.com (N.X.); xrmeng@aust.edu.cn (X.M.); hqchang@aust.edu.cn (H.C.)
* Correspondence: xiqwang@aust.edu.cn; Tel.: +86-15309648996
Abstract:
Gas accidents threaten the safety of underground coal mining, which are always accompa-
nied by abnormal gas concentration trend. The purpose of this paper is to improve the prediction
accuracy of gas concentration so as to prevent gas accidents and improve the level of coal mine safety
management. Combining the LSTM model with the LightGBM model, the LSTM-LightGBM model is
proposed with variable weight combination method based on residual assignment, which considers
not only the time subsequence feature of data, but also the nonlinear characteristics of data. Dur-
ing the data preprocessing, the optimal parameters of gas concentration prediction are determined
through the analysis of the Pearson correlation coefficients of different sensor data. The experimental
results demonstrate that the mean absolute errors of LSTM-LighGBM, LSTM and LightGBM are
1.94%, 2.19% and 2.77%, respectively. The accuracy of LSTM-LightGBM variable weight combination
model is better than that of the two above models, respectively. In this way, this study provides a
novel idea and method for gas accident prevention based on gas concentration prediction.
Keywords:
coal mine safety; LSTM; LightGBM; LSTM-LightGBM variable weight combination; gas
concentration prediction
1. Introduction
Energy is the engine of economic development and the lifeblood of national econ-
omy [
1
]. Coal is crucial with respect to the energy strategy of China, which is also caused
by the feature of resource distribution in China, but also it determines that the solution to
energy problems should depend on coal. For a long time, safety has always been one of the
important issues during the process of coal mining. Gas accidents are a particularly serious
problem. Through the investigation and analysis of coal mine gas accidents, it is found
that not accurately grasping the law of gas concentration changes is the main reason for
gas accidents [
2
]. Thus, if the inner rules can be explored and the gas concentration can be
predicted relatively accurately [
3
], it will be of great importance to reduce the occurrence of
gas accidents.
So far, many domestic and foreign scholars have conducted a great amount of research
on gas concentration prediction [
4
]. Normally, gas concentration prediction methods can be
broadly divided into two categories, one of which is using gas geomathematical modeling
methods, and the other of which is based on machine learning methods. However, since
the change of gas concentration is not a simple static process, and there are highly complex
nonlinear relationship among its the influencing factors, it is still a great challenge for the
current gas concentration prediction models to predict gas concentration accurately and
efficiently [5].
The prediction of gas concentration using the gas geomathematical model requires
detailed measurements of multidimensional attributes of the geological environment sur-
rounding the mine and the underground environment, such as mining depth, permeability
of coal seam, stability of coal seam and thickness of the coal seam. Wang et al. [
6
] con-
structed the gas concentration prediction equation based on one-dimensional regression
Energies 2022, 15, 827. https://doi.org/10.3390/en15030827 https://www.mdpi.com/journal/energies
