Application of RBF neural network model optimized by K-means and QGA in water conducted fractured zone height prediction

The selection accuracy of the water conducted fractured zone height directly affects the safety of mining under the water body.To accurately predict the height of water conducted fractured zone, this paper constructs the basic model of RBF neural network, then optimizes the basic model with QGA quantum genetic algorithm and K-means algorithm, and obtains K-means and QGA optimized RBF neural network height prediction model of water conducted fracture zone.The model is trained and tested.The results show that the accuracy of the model meets the requirements of engineering accuracy, and compared with the PSO-RBF neural network, the accuracy is higher and the convergence speed is faster.