RSNN-Based Instability Disaster Prediction of Tailings Dam

The instability disaster prediction model of tailings dam had been established, based on system analysis of the factors that caused the instability disaster of tailings dam, by selecting 6 prediction index, medium unit weight, cohesion, internal friction angle, slope angle, slope height and pore pressure ratio and combining with using theory of the rough set and neural network. First the rough set theory was used for the creation of decision table, data mining, attribution importance ranking and reducing, then the decision table processed by rough set theory the table was used as the input of the neural network and the algorithm of back propagation was used to train the prediction model. It was shown that the prediction values output by the model agrees well with the actual value and the accuracy of prediction was high. Research showed that the mathematics prediction method overcomes the bottleneck of neural network in slowing training efficiency and low prediction accuracy, providing an optimization method for risk prediction of tailings dam.