摘要
针对河川径流成因复杂性和水文过程随机性的特点,且用单一预测法存在一定局限性的现状,提出混合Bayes-Markov预测模型。先用Bayes公式对径流进行丰枯分类,然后采用加权Markov分析方法建立预测模型,该模型可综合利用Bayes和Markov方法的优点,提高径流预测精度。以兰州站河川径流量预测为例,进行模型验证。结果表明,2003～2009年径流量预测精度达到85.7%,能满足规范要求。
Aiming at the characteristics of the complexity of runoff cause and randomness of hydrological processes,and limitation of a single prediction method applied,a new method,called Bayes-Markov combined model,is presented based on Bayes theory and Markov theory.This paper attempts to use the Bayes formula to classify the low high annual runoff firstly,then to create forecasting model with the weighted Markov analysis method.The two prediction methods were scientifically combined,which generalizes advantages of the ones and raises the accuracy of runoff prediction.The prediction model was identified by taking prediction of annual runoff variation in Lanzhou Station of Yellow River Basin.The results show that the predicted values from 2003 to 2009 meet the requirements of the Specifications,and the accuracy of it was 85.7%.
出处
《水利科技与经济》
2011年第12期1-4,共4页
Water Conservancy Science and Technology and Economy
基金
河南省教育厅自然科学研究计划项目(2010B570002)
