Bayesian networks precipitation model based on hidden Markov analysis and its application

查看全文 作　　者：WANG HongRui1, YE LeTian2, XU XinYi1, FENG QiLei3, JIANG Yan1, LIU Qiong1 & TANG Qi1 1 College of Water Sciences, Key Laboratory for Water and Sediment Sciences of Ministry of Education, Beijing Normal University, Beijing 100875, China;2 School of Mathematical Sciences, Peking University, Beijing 100871, China;3 School of Science, Beijing Institute of Education, Beijing 100011, China

出　　处：《Science China(Technological Sciences)》索引2010年第53卷第2期,共9页

基　　金：supported by the National Hi-Tech Research and Development Program of China ('863' Project) (Grant No. 2006BAB04A08) 摘　　要：Surface precipitation estimation is very important in hydrologic forecast. To account for the influence of the neighbors on the precipitation of an arbitrary grid in the network, Bayesian networks and Markov random field were adopted to estimate surface precipitation. Spherical coordinates and the expectation-maximization (EM) algorithm were used for region interpolation, and for estimation of the precipitation of arbitrary point in the region. Surface precipitation estimation of seven precipitation stations in Qinghai Lake region was performed. By comparing with other surface precipitation methods such as Thiessen polygon method, distance weighted mean method and arithmetic mean method, it is shown that the proposed method can judge the relationship of precipitation among different points in the area under complicated circumstances and the simulation results are more accurate and rational. 关键词：surface PRECIPITATION MARKOV RANDOM field BAYESIAN networks EM algorithm, QINGHAI LAKE

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Bayesian networks precipitation model based on hidden Markov analysis and its application

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