Interval combined prediction of mine tunnel's air volume considering multiple influencing factors. - Zhen Wang; Erkan Topal; Liangshan Shao; Chen Yang (PLoS ONE)
Continuous monitoring and accurate measurement of required air volume in mine tunnels are crucial phenomena for mine safety However, air volume fluctuates and can become unstable which can lead to biased measurement in underground environment. In this paper, to accurately measure the mine tunnel air volume, the tunnel air volume, and related ventilation parameters are consistently monitored, and the real monitoring data is converted to interval numbers for representation. These interval numbers are then preprocessed using an Interval-type Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(In-CEEMDAN) to extract the essential features of the data. Then, the monitored data is processed using the phase space reconstruction technique to identify the most relevant influencing factors related to the air volume. The tunnel air volume and influencing factors are then input into different neural networks for air volume prediction. To further improve prediction accuracy, the predicted values of wind volume intervals from the single prediction method are transformed into triangular fuzzy numbers, and the generalized induced ordered weighted average operator is introduced for the combination of prediction results. The grey correlation method is selected as the optimization criterion, and the preference coefficients are used to transform the multi-objective optimization problem into a single-objective optimization problem. In order to reduce the prediction error, the L2 paradigm is combined with the gray correlation to construct a complete interval combination type air volume prediction model which considers multiple influencing factors. Finally, a sensitivity analysis was carried out to analyze the values of the preference coefficients in the model, and the final range of values was given. Experimental analysis using data from a coal mine in Inner Mongolia showed that the method could reduce Combined Weighted Mean Absolute Error(CWMAE) to a maximum of 5.0384, Combined Weighted Root of Mean Squares Error(CWRMSE) to 6.8889, and Combined Weighted Mean Absolute Percentage Error(CWMAPE) to 1.4756, which indicates that the method proposed in this study can effectively improve the prediction accuracy of the mine tunnel air volume.