Research Of Markov Chain Prediction Method And Its Application On Hydrology Series

Firstly, the "Markov property" test of random variable series was discussed, then the Markov chain prediction method based on absolute distribution (ADMCP), the Markov chain prediction method based on probability summation (SPMCP) and weighted Markov chain prediction method (WMCP) were discussed. Especially the weighted Markov chain prediction method was studied and completed in the dissertation. The bright point of this dissertation was that various Markov chain prediction methods were studied and compared, and various factors influenced on accuracy of Markov chain predictions based on statistical experiments. Through stochastic simulation, a series results were obtained as follows:(1) The prediction accuracy of WMCP method is the highest, the prediction accuracy of ADMCP method is the lowest, and the prediction accuracy of SPMCP method is in the middle.(2) The time of statistical experiments c has a kind of influence on prediction results, and the prediction accuracy approached stable when c ≥200.(3) The prediction accuracy of various Markov chain prediction methods had a suitability to difference distributions.(4) The coefficients Cv and Cg had a heavy influence on prediction accuracy. When Cv increased, the prediction biases a little increased, and when Cg increased, the prediction biases decreased. But the prediction results of WMCP method were the most robust.(5) It would increase prediction accuracy when sample size enlarged, which was similar to any efficient statistical prediction method. The goodness of these three kinds Markov chain prediction methods were not changed with sample size, that is that various Markov chain prediction methods ail have a auto consistent property.(6) The change of a step self-correlation coefficient px(l)had a kind of influence on various Markov chain predictions accuracy, but the influence was not so significant.(7) The classification of observed values from a Markov series had a strong influence on prediction accuracy, whether or not that the series had a P-III distribution or three parameters LN distribution.Moreover, the application of WMCP method on hydrology and water resources was studied in the dissertation. The following three real instances were discussed: The application of WMCP method to the prediction of river runoff state, the application of WMCP method to the prediction of precipitation state, and the application of WMCP method to the prediction of plum rains intensity. Three real instances all indicated that WMCP method was a valuable prediction method.