Predicting Annual Precipitation Using the Weighted Markov Chain Solved by the Improved FCM Algorithm
MIAO Zhengwei
XU Ligang
Abstract：We predicted the annual precipitation from 1951 to 2015 in Chengde using the improved FCM algo-rithm.The precipitation series was firstly classified using the fuzzy clustering, from which 10 cluster centers and their associated membership matrix were obtained. The weighted Markov chain model was established based on the maximum membership principle and the calculated annual precipitation series, by using the standardized auto-correlation coefficients as the weights. We used the membership vector as the initial state vector and predicted the annual precipitation from 2004 to 2015 in Chengde;the calculated results agree well with the measurements. We introduced and modified the level characteristic value formula of the fuzzy set. Based on the predicted results from the Markov chain model, we predicted the annual precipitation from 2004 to 2015 using the modified level characteristics value formula. The relative error was less than 7%. The preliminary results show that theproposed model is reliable for predicting annual precipitation.
Keywords：predictionannualprecipitationfuzzy clusteringFCM algorithmweighted Markov chainChengde
Publication Date：2017-01-01
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：8( 114-121 )