Bayesian updating of a prediction model for sewer degradation
Bayesian updating of a prediction model for sewer degradation
复制标题
下水道退化预测模型的贝叶斯更新
DOI：
10.1080/15730620701737157
复制
发表时间：
2008
影响因子：
2.7
通讯作者：
J. M. van Noortwijk
中科院分区：
文献类型：
--
作者：
H. Korving;J. M. van Noortwijk
下水道退化主要是一个随机过程。可以使用基于状态状态的模型来预测未来的下水道状态。在荷兰，将专家意见和目视检查相结合，正在开发SPIRE模型。在该模型中，条件状态的似然函数随检查而更新。狄利克雷分布用于描述主观的先验知识，即专家知识。结果表明，该模型可以进行解析求解，减少了计算时间。此外，专家和检查的权重是根据先验信息和数据确定的，而不是根据主观专家知识估计的。
Sewer degradation is mainly a stochastic process. The future condition of sewers can be predicted using models based on condition states. In The Netherlands, the SPIRIT model is being developed combining expert opinion and visual inspections. In this model the likelihood function of condition states is updated with inspections. A Dirichlet distribution is used to describe ‘subjective’ prior knowledge, i.e. expert knowledge. The results show that the model can be solved analytically reducing calculation time. In addition, the weight of experts and inspections is determined on the basis of prior information and data instead of estimated by subjective expert knowledge.