Gravitational force based coal floor water inrush prediction algorithm

In order to forecast water inrush, many experiments had been performed, and numerous methods were proposed. Most of the traditional machine learning methods need a large number of training samples to train predictive models. However, collecting a large number of training samples in coal water inrush hazard is technically almost impossible. Combining the actual situation of a real coal mine in Shanxi Province, a novel semi-supervised gravitational force based water inrush prediction algorithm was proposed. In the proposed algorithm the principle of gravitational force was employed in the predictive model. Small training samples were used as attracting sources to attract testing sample, and further to propagate the label to testing sample. Simulation results show that with limited training samples the proposed methods can achieve a good accuracy in terms of predicting. The proposed methods are further applied in solving the water-irruption prediction problem on the coal seam floor. Results from both historical data and real water inrush data demonstrate that the gravitational based water inrush prediction algorithm could be regarded as a practical and effective approach in confronting the circumstance of limited valid data. © 2015, China Coal Society. All right reserved.