Bayesian updating of a prediction model for sewer degradation
نویسندگان
چکیده
Sewer degradation is mainly a stochastic process. The future condition of sewers can be predicted with models based on condition states. In The Netherlands, the ‘SPIRIT’ model is being developed which combines expert opinion and visual inspections to predict sewer degradation. The statistical method implemented in this model is based on Bayesian statistics. The likelihood function of condition states is updated with new sewer inspections. A Dirichlet distribution is used to describe ‘subjective’ prior knowledge. Initially, prior knowledge only consists of expert opinions. The application of the statistical method is illustrated with an example. The results show that the model can be solved analytically, which reduces calculation time. In addition, expert opinions and inspections can be combined very easily and at any moment. Finally, the weight of experts and inspections is determined on the basis of the prior information and data instead of estimated by means of subjective expert knowledge.
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