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

Abstract

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.