Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine
Yang Hongmei
Transactions on Machine Design, Vol. 3, No. 2, 2015.
DOI: 
Link: http://www.dline.info/tmd/fulltext/v3n2/v3n2_3.pdf
Abstract:
It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining.
Owing to the high-frequency, non-stationary, 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 employ 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 a real-time one-step or multi-step ahead prediction.