%0 Journal Article
%A MIAO Zheng-wei
%A XU Li-gang
%T Prediction of Annual Precipitation by Weighted Markov ChainBased on Membership Modification
%D 2018
%R 10.11988/ckyyb.20160893
%J Journal of Yangtze River Scientific Research Institute
%P 40-46
%V 35
%N 1
%X The annual precipitation series of Yulin city from 1951 to 2015 was divided into 9 states by the Fisher optimal partition method. The weighted Markov chain model was established by taking the standardized autocorrelation coefficients as weights. With the mean value of all precipitation in the same state as the cluster center, the membership function of the Fuzzy C-Means was applied to calculate the membership of annual precipitation, and the membership vector was taken as the initial state vector for the time period. The precipitation state from 2006 to 2015 in Yulin city was predicted year by year. All the results agree with the reality. Based on the prediction results of Markov Chain, the precipitation was predicted respectively from 2006 to 2015 by the level characteristics value of Fuzzy Sets, and the relative error of all the prediction results is less than 10%. The preliminary results show that the  model of weighted Markov chain based on membership modification is feasible.
%U http://ckyyb.crsri.cn/EN/10.11988/ckyyb.20160893