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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 858260, 8 pages
Research Article

Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine

1School of Information and Electrical Engineering, China University Mining & Technology, Xuzhou, Jiangsu 221116, China
2School of Medical Informatics, Xuzhou Medical College, Science and Technology Building E206, Dong Dian Zi Campus, Xuzhou, Jiangsu 220009, China
3Northern Nenghua Company of Wanbei Coal-Electricity Group, Huaibei, Anhui 235000, China
4School of Medical Imaging, Xuzhou Medical College, Xuzhou, Jiangsu 221009, China

Received 18 February 2014; Accepted 5 July 2014; Published 22 July 2014

Academic Editor: Marcelo J. Colaço

Copyright © 2014 Wu Xiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the high-frequency, nonstationary fluctuations and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model that integrates wavelet transform and extreme learning machine (ELM) termed as WELM (wavelet based ELM) for coalmine gas concentration is proposed. Firstly, the proposed model employs Mallat algorithm to decompose and reconstruct the gas concentration time series to isolate the low-frequency and high-frequency information. Then, ELM model is built for the prediction of each component. At last, these predicted values are superimposed to obtain the predicted values of the original sequence. This method makes an effective separation of the feature information of gas concentration time series and takes full advantage of multi-ELM prediction models with different parameters to achieve divide and rule. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in one-step or multistep ahead prediction.