Abstract
Artificial neural network (ANN) provides a new way for mine water inflow prediction. However, the effectiveness of prediction using ANN model would not be guaranteed if the influencing factors of water inflow are difficult to quantify or there are only a few observation data. Chaos theory can recover the rich dynamic information hidden in time series. By reconstructing water inflow time series in phase space, the multi-dimensional matrix could be obtained, with each column representing an influencing factor of water inflow and its value representing the change of the influencing factor with time. Therefore, a new prediction model of mine water inflow can be established by combining ANN with chaos theory when lacking data on the influencing factors of water inflow. In the present study, the No. 12 coal mine of Pingdingshan China was selected as the study site. The Chaos-GRNN model and Chaos- BPNN model of mine, water inflow were established by using the water inflow data from February 1976 to December 2013. The model was verified by using the water inflow values in the 24 months from 2014 to 2015. The number embedded dimension (M) of influencing factors of water inflow determined by phase space reconstruction was 7, meaning that there were 7 influencing factors of water inflow and 7 neurons in GRNN input layer, and the time delay was 13 months. The value of GRNN input layer neurons was determined accordingly. The maximum Lyapunov index was 0.0530, and the prediction time of GRNN was 19 months. The two models were evaluated by using four evaluation indices (R, RMSE, MAPE, NSE) and violin plot. It was found that both models can realize the long-term prediction of water inflow, and the prediction effectiveness of Chaos-GRNN model is better than that of Chaos-BPNN model.
Highlights
In China, more than 200 water inrushes from Ordovician limestone have occurred in North China coalfields over the past 40 years, resulting in economic losses of more than 30 billion yuan and 1300 deaths [22, 25].Water inrush problems in deep mining become increasingly serious as the mining depth increases
This study combined chaos theory with Generalized regression neural networks (GRNN), using reconstructed phase space of mine water inflow time series to determine the number of GRNN input layer neurons and their values, establishing Chaos-Generalized Regression Neural Network (Chaos-GRNN) model to achieve a more effective prediction model of mine water inflow
Chaos theory can recover the rich dynamic information hidden in time series
Summary
In China, more than 200 water inrushes from Ordovician limestone have occurred in North China coalfields over the past 40 years, resulting in economic losses of more than 30 billion yuan and 1300 deaths [22, 25].Water inrush problems in deep mining become increasingly serious as the mining depth increases. This study combined chaos theory with GRNN, using reconstructed phase space of mine water inflow time series to determine the number of GRNN input layer neurons and their values (reducing the subjectivity and limitations of the original GRNN), establishing Chaos-Generalized Regression Neural Network (Chaos-GRNN) model to achieve a more effective prediction model of mine water inflow. This model is used to predict the mine water inflow for a long time (24 months) to verify practicality of the model.
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