Intelligent Forecasting Approaches Of Development Trends Of Coal Gas Monitoring Data

The aim is to make clear the forecaster main input factors of coal mine underground gasconcentration, gas emission quantity, coal and gas outburst, etc, reduce the complexity of theirsamples and simplify the samples attributes. It is also for evaluation the influence of the inputfactors uncertainties on the forecasting results as well as accurate prediction the gas hazardeven when the effect of the related factors changes. The following observations have beenmade:For recognition the main input factors of gas concentration and gas emission quantity, ect,an approach related to prediction precision to select the main input factors of gas hazardforecasting is proposed. The necessary elimination or adding of input factors are determinedby F test for the forecasting variances before and after elimination or adding. Afterinvestigating on all cases, the most suitable input factor under the current geologicalenvironment and other conditions can be identified, that is the main input factor combinationof the most improved prediction performance.With regard to the situation that time series of gas concentration and gas emissionquantity often show strong randomness and complexity, an approach based on wavelettransform is put forward to predict gas concentration, gas emission quantity and other timeseries. Wavelet decomposition is used to reduce the complexity of time series, and the waveletcomponents are transformed into new samples with historical characteristics, which are usedin the prediction, respectively. The final forecasting results are obtained by combining all ofthe predicted results. The energy of time series decomposed into wavelet or scaling functionalspace is used as the scaling energy, based on which, the method calculation best waveletdecomposition level is presented according to the ratio of the scaling energy and the largestforecasting biases energy under the allowed prediction precision. The optimized parameters of forecasting model is obtained by a Particle Swarm Optimization based program.Concerning the distribution of coal mine underground gas having fuzziness, uncertaintyand other characteristics, an approach is suggested to forecast gas concentration and gasemission quantity based on Fuzzy C-means Clustering (FCM). FCM is introduced to classifythe samples into different categories. The corresponding forecaster is constructed for eachcategory, respectively. The parameters of samples space, predictor and FCM are optimizedbased on the ant colony optimization algorithm by using F test of the prediction residual asthe fitness value.In order to evaluate the influence of the input variables uncertainties on the forecastingresults of gas concentration and gas emission quantity, ect,, an estimation approach based onSampled-Blind-Number (SBN) is proposed, based on which, the problem of processing theprobability distribution with various types is solved. The transfer algorithm of the relatedfactors uncertainties in forecasting, the way of high order SBN converting lower order oneand the method of the probability distribution and the confidence interval of the forecastingresults with the input factors having uncertainty are put forward.To accurately predict the gas concentration and gas emission quantity even when theeffects of the related factors change, an approach based on the virtual state variables andself-adaptive network structure is proposed. The input variables with the capability ofreflecting the awaiting forecasting objec are mapped by a pattern recognition network, and theobtained characteristics responses of the output space are used as the virtual state variables.The awaiting forecasting objec is predicted by the virtual state variables based forecaster. Thestructure of the pattern recognition network is adjusted according with the change of theawaiting forecasting object such as gas concentration by the feedback mechanism basedforecasting biases. Consequently, the suitable virtual state variables which can adapt to thechanges of the related factors effect are obtained.Based on the research achievements in this paper, the above methods are applied in someexamples, and gas concentration, gas emission quantity, coal and gas outburst are predicted.The results show that the forecasting performance is remarkably improved indicating that theproposed approaches are feasible and effective.