摘要
贝叶斯网络是目前人工智能中不确定知识与推理中最有效的理论模型之一。提出一种基于动态贝叶斯网络模型理论的水文预报方法。在综合考虑降雨径流成因的基础上,利用领域专家知识构建网络模型,在已有降雨、流量数据的基础上通过计算变量间的条件概率来计算流量发生的可能性。最后,通过渭河流域咸阳至临潼段历时数据进行仿真实验,对仿真结果和该模型进行了分析。
Bayesian network is one of the most efficient models in the uncertain knowledge and reasoning field.A rainfall-runoff prediction model based on dynamic Bayesian network is put forward in this paper.The network model is based on knowledge of the field experts and the causes of rainfall-runoff, they can produce the probability of the flow rate by calculating the conditional probability among variables on the basis of historical rainfall and runoff data.Finally through the simulation of historical data about Wei He river basin from Xianyang gauge station to Lintong gauge station,the model and the results are analyzed.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第6期231-234,共4页
Computer Engineering and Applications
基金
国家高技术研究发展计划(863)(No.2006AA01A126)
国家自然科学基金(No.50279041)~~
