Mine water inrush prediction based on cloud model theory and Markov model
Shi Shi-liang · Journal of Central South University(Science and Technology) · 2012
A new model was proposed to predict the mine water inrush considering the randomness and fuzziness called cloud-weighed Markov model.Firstly,cloud model theory was used to classify the state concept of mine water inrush.Then the X-term cloud generation was used to gain the state of every training sample according to great determination method.Due to randomness and fuzziness of cloud model,output state of every training sample may be different,but belongs to no more than two ones,different state spaces of Markov chain were developed for every situation.After a certain number of simulations,different occurrence probabilities of different state spaces were regarded as the weight to calculate the final prediction probabilities.According to the final prediction probabilities,state of sample for prediction was determined by the principle of maximum membership.Finally,the mine water inrush of the 4th Mine in Hebi in the years from 1982 to 1999 was taken as training samples of time series and the water inrush in 2000-2001 was forecasted with the established model.The results show that states of prediction in 2000-2001 are both state 5,which coincide with their real states,and the two years belongs to less water inrush years.